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7/31/2011
11/19/2009-9/30/201
5a. CONTRACT NUMBER
4. TITLE AND SUBTITLE
"Development of the University Center for Disaster Preparedness and Emergency
Response (UCDPER)"
5b. GRANT NUMBER
W9132T-10-1-0001
5c. PROGRAM ELEMENT NUMBER
6. AUTHOR(S)
5d. PROJECT NUMBER
Lacy, Clifton, R, MD; Laskin, Jeffrey, D., PhD; Isukapalli, Sastry, PhD; Fefferman,
Nina, PhD; Altiok, Tayfur, PhD; Jafari, Mohsen, PhD; Eisenstein, Robert, MD; Geria,
Rajesh, MD, Arya, Rajiv, MD; Balaguru, Perumalsam, PhD; Greenberg, Michael,
PhD; Lahr, Michael, PhD; Garabaglu, Mohsen, PhD; and DiFernando, George, MD
5e. TASK NUMBER
5f. WORK UNIT NUMBER
8. PERFORMING ORGANIZATION
REPORT NUMBER
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
Rutgers, The State University, ORSP, 3 Rutgers Plaza, New Brunswick, NJ 08901
U MDNJ-Robert Wood Johnson Medical School
Robert Wood Johnson University Hospital
10. SPONSOR/MONITOR'S ACRONYM(S)
9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)
Construction Engineering Research Laboratory of the Engineer Research and Development
Center
Champaign, Illinois
CERL-ERDC
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Approved for public release; distribution is unlimited.
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13. SUPPLEMENTARY NOTES
14. ABSTRACT
The objective of the overall project was to develop a center of excellence in disaster preparedness and emergency response, linking together three
major institutions and gaining research, education, and clinical synergies from the collaborations between their subject matter experts. The
University Center for Disaster Preparedness and Emergency Response (UCDPER) has been established as ajoint initiative of Rutgers, The State
University of New Jersey, UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson University Hospital. UCDPER's missions
include protection of the lives, health and well-being of the general public, vulnerable populations and the workforce - and protection of societal,
economic and physical infrastructure - through research, education, community outreach and clinical advances in preparedness/response to
all-hazards emergencies, disasters, and terrorism. The research projects conducted under the UCDPER umbrella have produced recommendations
guidelines, and models focused on maximizing effectiveness and efficiency of disaster preparedness and emergency response in all-hazards
scenarios. Collaboration across the three partner institutions has become robust over the course of the project. Follow-up projects are being planned
15. SUBJECT TERMS
Disaster preparedness, emergency response.
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a. REPORT b. ABSTRACT c. THIS PAGE
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Final Technical Report
Prepared for: Construction Engineering Research Laboratory (CERL)
Engineer Research and Development Center (ERDC)
U.S Army Corps of Engineers
2902 Newmark Drive, PO. Box 9005
Champaign, Illinois 61826-9005
Grant #:
W9132T-10-1-0001
Date:
September 30, 2011
Grant Title:
Development of the University Center for Disaster Preparedness and
Emergency Response (UCDPER)
A collaborative initiative of:
Rutgers, The State University of New Jersey (RU)
UMDNJ-Robert Wood Johnson Medical School (UMDNJ-RWJMS)
Robert Wood Johnson University Hospital (RWJUH)
Grant Principal Investigator:
Clifton R. Lacy, M.D. (RUTGERS/UMDNJ-RWJMS/RWJUH)
Project Principal Investigators:
Dr. Tayfur Altiok
Dr. Rajiv Arya
Dr. Perumalsamy Balaguru
Dr. George DiFerdinando
Dr. Robert Eisenstein
Dr. Nina Fefferman
Dr. Mohsen Garabaglu
Dr. Rajesh Geria
Dr. Michael Greenberg
Dr. Sastry Isukapalli
Dr. Mohsen Jafari
Dr. Michael Lahr
Dr. Jeffrey D. Laskin
(RUTGERS)
(RWJUH)
(RUTGERS)
(UMDNJ-SPH)
(RWJUH)
(RUTGERS)
(SYSTEMATIC CONCEPTS)
(RWJUH)
(RUTGERS)
(UMDNJ-RWJMS)
(RUTGERS)
(RUTGERS)
(UMDNJ-RWJMS)
Disclaimer:
The views, opinions, positions, conclusions, or strategies in this work are those of the authors
and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or official
policy or position of the Department of Defense or any agency of the U.S. government and no
official endorsement should be inferred.
Abstract
The objective of the overall project was to develop a center of excellence in disaster
preparedness and emergency response, linking together three major institutions and gaining
research, education, and clinical synergies from the collaborations between their subject matter
experts.
The University Center for Disaster Preparedness and Emergency Response (UCDPER)
has been established as a joint initiative of Rutgers, The State University of New Jersey,
UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson University Hospital.
UCDPER's missions include protection of the lives, health and well-being of the general public,
vulnerable populations and the workforce - and protection of societal, economic and physical
infrastructure - through research, education, community outreach and clinical advances in
preparedness and response to all-hazards emergencies, disasters, and terrorism.
The research projects conducted under the UCDPER umbrella have produced
recommendations, guidelines, and models focused on maximizing effectiveness and efficiency of
disaster preparedness and emergency response in all-hazards scenarios.
Collaboration across the three partner institutions has become robust over the course of
the project. Follow-up projects are being planned. The novel findings of this overall effort and
its projects will be disseminated through publication and presentation.
11
Foreword
This project has been sponsored by the University Center for Disaster Preparedness and
Emergency Response (UCDPER) - A Collaborative Initiative of Rutgers, The State University
of New Jersey, UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson
University Hospital - with support from Department of Defense Grant Number W9132T-10-10001.
The Grant Officer's Technical Representative has been Stuart D. Foltz, ERDC-CERL,
ATTN: CECER-CF-M (Mr. Stuart Foltz), P.O. Box 9005, Champaign, IL 61826-9005.
The Contract Specialist has been Andrea J. Krouse, ERDC-CERL, ATTN: CEERD-CTC
(Ms. Andrea Krouse), P.O. Box 9005, Champaign, IL 61826-9005.
The work has been administered by the Construction Engineering Research Laboratory
(CERL) of the Engineer Research and Development Center (CERL-ERDC), and the Office of
Naval Research (ONR).
The Grant Principal Investigator was Clifton R. Lacy, M.D., (RUTGERS/UMDNJRWJMS/RWJUH).
Project Principal Investigators were Dr. Tayfur Altiok (RUTGERS), Dr. Rajiv Arya
(RWJUH), Dr. Perumalsamy Balaguru (RUTGERS), Dr. George DiFerdinando (UMDNJ-SPH),
Dr. Robert Eisenstein (RWJUH), Dr. Nina Fefferman (RUTGERS),
Dr. Mohsen Garabaglu (SYSTEMATIC CONCEPTS), Dr. Rajesh Geria (RWJUH),
Dr. Michael Greenberg (RUTGERS) Dr. Sastry Isukapalli UMDNJ-RWJMS), Dr. Mohsen Jafari
(RUTGERS), Dr. Michael Lahr (RUTGERS), and Dr. Jeffrey D. Laskin (UMDNJ-RWJMS).
in
Table of Contents
List of Tables
v
INTRODUCTION
1
Background
I
Objective
3
Approach
3
Mode of Technology Transfer
4
CONCLUDING CHAPTER
4
Summary
4
Conclusions
6
Recommendations
7
CHAPTER 1: Project 09 OIF: Biomarkers of Exposure to Chemical Terrorism Agents
11
CHAPTER 2: Project 09 02 F: Interpretation of Prospective Exposure Studies
Completed in New York City for Community Catastrophic Planning
CHAPTER 3: Project 09 04 F: Emergency Shelter Location and Resource Allocation
22
42
CHAPTER 4: Project 09 05 F: Supply Chain of Critical Medical Resources for
Emergency Situations
87
CHAPTER 5: Project 09 06 F: Patient Flow Optimization under Regular and
Emergency Hospital Operations
125
CHAPTER 6: Project 09 08 F: Use of Ultrasound in the Emergency Setting to Improve
Triage of Trauma Patient
148
CHAPTER 7: Project 09 10 F: Strengthening Windshield Resistance
IV
159
CHAPTER 8: Project 09 11 P: Building a Decision Support Tool for Studying the
Economic Impact of Loss of Passenger Rail Service: A Prototype of New Jersey's
Urban Industrial Corridor
182
CHAPTER 9: Project 09 12 P: Intelligent Demand Assigned Networks Cost and
Performance
220
CHAPTER 10: Project 09 13 F: Bridging the Gaps Between Public Health, the Health
Care System, and First Responders
TABLE
288
8
List of Tables
Table 1: List of Research Projects
8
VI
INTRODUCTION
Background
The University Center for Disaster Preparedness and Emergency Response (UCDPER) is a
joint initiative of Rutgers, The State University of New Jersey (RUTGERS), UMDNJ-Robert
Wood Johnson Medical School (UMDNJ-RWJMS), and Robert Wood Johnson University
Hospital (RWJUH). The overall effort was funded by the Construction Engineering Research
Laboratory (CERL) of the Engineer Research and Development Center (ERDC) of the U.S.
Army Corps of Engineers under Department of Defense Grant Number W9132T-10-1-0001.
Missions of University Center for Disaster Preparedness and Emergency Response are:
•
to protect the lives, health, and well-being of the general public, vulnerable
populations, and the workforce, and
•
to protect the societal, economic, and physical infrastructure of New Jersey and the
nation, through
•
research, education, community outreach, and clinical advances in
preparedness and response to all-hazards emergencies, disasters, and terrorism.
UCDPER is founded upon the strengths and successes of our three parent institutions in a
unique partnership that includes world-class scientific strengths in disaster medicine, trauma,
exposure science, toxicology, engineering, and mathematics/computer science.
UCDPER builds on highly successful interdisciplinary activities including Level 1
Trauma Center at RWJUH/UMDNJ-RWJMS; the EPA Center on Exposure and Risk
Modeling at UMDNJ-RWJMS; the NIH CounterACT Center at UMDNJ-RWJMS; the Center
for Advanced Infrastructure and Transportation (CAIT) at Rutgers; the National
Transportation Security Center of Excellence (NTSCOE) at Rutgers; the International
Center for Terror Medicine (ICTM) at RWJUH; the DHS Center of Excellence for Command,
Control, and Interoperability (CCICADA) at Rutgers, and the DHS University Center of
Excellence in Dynamic Data Analysis (DyDAn) at Rutgers.
UCDPER links together subject matter experts from our three parent institutions creating a
broad spectrum of multidisciplinary capabilities in all-hazards preparedness and response. This
combination of efforts and strengths makes our Center a unique resource for New Jersey and the
nation. Furthermore, our Center works closely with the Federal Departments of Defense, Health
and Human Services, Energy, Environmental Protection, and Homeland Security, and with the
New Jersey Office of Homeland Security and Preparedness, the New Jersey Department of
Health and Senior Services, and the New Jersey Domestic Security Preparedness Task Force.
New Jersey is the home base for UCDPER. Strategically located between New York City
and Philadelphia, New Jersey is the most densely populated state in the nation and is home to a
major portion of the Northeast corridor of roadway and railway transit and transportation. New
Jersey has a significant history of natural and man-made, accidental and intentional disasters and
health emergencies. In addition, New Jersey has infrastructure vital to national security and
economic stability including major industries and utilities, military bases, financial and
commercial centers, population concentration points, and transportation/transit links.
The UCDPER's Executive Committee convenes on a monthly basis or more often if needed
to discuss current events, issues, and projects and to provide guidance and recommendations.
The Executive Committee is composed of the following members:
•
Clifton R. Lacy, M.D.
(RUTGERS/UMDNJ-RWJMS/RWJUH)
•
Fred S. Roberts, Ph.D.
(RUTGERS)
•
Ali Maher, Ph.D.
(RUTGERS)
•
Michael R. Greenberg, Ph.D. (RUTGERS)
•
Vicente H. Gracias, M.D.
(UMDNJ-RWJMS)
•
Paul J. Lioy, Ph.D.
(UMDNJ-RWJMS)
•
Jeffrey D. Laskin, Ph.D.
(UMDNJ-RWJMS)
•
Judith E. Burgis, M.S.
(RWJUH)
•
Michael Antoniades, M.P.A. (RWJUH)
•
Robert Eisenstein, M.D.
(RWJUH)
The findings derived from the Research and Development activities of the Center and from
the Clinical and Health Care Preparedness and Response activities of the Center are establishing
scientific advances that will improve both military and civilian disaster preparedness and
emergency response.
Objective
The research conducted at Rutgers University and its partners (UMDNJ-RWJMS and
RWJUH) is under the umbrella of the University Center for Disaster Preparedness and
Emergency Response (UCDPER).
The various research efforts conducted under this grant are described in this document. The
overall objective was to develop a center of excellence in disaster preparedness and emergency
response.
Approach
Phase I of the current project consisted of thirteen research efforts which are presented along
with their respective abstracts on Table 1: List of Research Projects. Ten of these projects have
been successfully completed and three are still in progress.
Detailed Technical Reports for each research effort are presented in chapters 1 through 10.
Mode of Technology Transfer
Explicitly-stated aims and missions of UCDPER include Research and Development as well
as the dissemination of research results to improve disaster preparedness and emergency
response. The UCDPER Executive Committee, Project Leaders, and Investigators will produce
journal quality scholarly articles and scientific presentations related to and highlighting Center
development and research activities.
CONCLUDING CHAPTER
Summary
The University Center for Disaster Preparedness and Emergency Response (UCDPER) is a
joint initiative of Rutgers, The State University, UMDNJ-Robert Wood Johnson Medical School,
and Robert Wood Johnson University Hospital. UCDPER's missions are protection of the lives,
health and well-being of the general public, vulnerable populations and the workforce - and
protection of societal, economic and physical infrastructure - through research, education,
community outreach and clinical advances in preparedness/response to all-hazards emergencies,
disasters and terrorism.
The objective of this overall effort was to develop a center of excellence in disaster
preparedness and emergency response.
The First Phase of the overall project has concluded with robust collaboration between
subject matter experts of the three partner institutions, ten projects successfully completed, and
three projects expected to be concluded by February 15, 2011.
The research projects conducted under the UCDPER umbrella have produced
recommendations, guidelines, and models focused on maximizing effectiveness and efficiency of
disaster preparedness and emergency response in all-hazards scenarios.
Fefferman's study showed that shelter location, resource allocation to shelters, and the
ability to rapidly and accurately communicate appropriate evacuation routes to the public will
drastically affect the success of public health preparedness/intervention strategies for heatevents. It further demonstrated the utility of coupling theoretical optimization strategies with
simulation-based methods to determine likely outcomes for public health interventions.
Greenberg and Lahr designed and tested economic simulation models to assess the impact of
regional economic impacts of hazards events on a major rail corridor and to estimate the costs
and benefits of making the system more capable of withstanding events and recovering from
them.
Cost containment and performance are major issues in Emergency Management (EM) and
Disaster Recovery (DR) network operations. Garabaglu's results demonstrated that considerable
cost savings and performance for EM/DR networks could be achieved when using more
advanced technology and access methods. An optimal and cost-effective solution based on
concepts of cloud communications and intelligent network architecture is recommended.
Laskin's study on biomarkers has provided significant new information on the mechanism of
action of vesicants that it is likely to lead to new medicines that can be used to treat individuals
exposed to these chemical agents.
Altiok addressed the issue of managing inventories of medical supplies and especially the
critical ones in hospitals under surge (pandemic) scenarios. The purpose of the study was to
provide guidelines to implement a formal procedure to help decision makers managing
inventories in the health care industry. A Dynamic Programming optimization model was
constructed to optimally manage the inventory of critical medical supplies in hospital settings.
The project on patient flow optimization and emergency hospital operations conducted by
Jafari concluded that real time information on patient tracking and resource availability can
significantly improve patient flow throughout hospitals.
Balaguru evaluated the levels of protection conferred by use of thin films to reduce blastrelated fragmentation and shattering of glass panels on response vehicles' windshields. Two
different protective films (VehicleGARD® and ShatterGARD®) were tested for their suitability
in emergency response situations. Both films tested better than the control sample; however,
VehicleGARD was more suitable for visibility after impacts.
The work on community catastrophic planning performed by Isukapalli and his team
provides a framework that will allow improvement of emergency planning and response.
Three education and training sessions related to all-hazards health threats resulting in mass
casualties were conducted and assessed under DiFerdinando's direction. These exercises were
performed in conjunction with federal, state, county, and local partners.
Collaboration across the three partner institutions has become robust over the course of the
project. Follow-up projects are being planned. The novel findings of this overall effort and its
projects will be disseminated through publication and presentation. A regional scientific meeting
of UCDPER, scheduled for December, 2011, will highlight the disaster preparedness and
emergency response research findings.
Conclusions
Our efforts have created a highly productive collaboration between three major institutions
in the State of New Jersey - Rutgers, the State University, UMDNJ-Robert Wood Johnson
Medical School and Robert Wood Johnson University Hospital - resulting in the development of
a multidisciplinary University Center for Disaster Preparedness and Emergency Response
(UCDPER) and the successful completion often important research projects aimed at improving
the capability and capacity to effectively and efficiently respond to a wide variety of disasters
and major emergencies. Three projects are still underway and their results will be reported when
completed. Conclusions of individual projects can be found in Chapters 1 through 10.
Recommendations
A number of the projects carried out have yielded important results upon which additional
research efforts should be based to further improve disaster preparedness and response.
Further collaborative efforts with institutions and investigators should be developed to
address issues of regional and national importance.
Efforts should be developed to increase the spectrum of research to include all hazards:
biological, chemical, radiological, nuclear, explosive and incendiary.
Widespread dissemination of the results to affiliated and unaffiliated subject matter experts
will maximize the impact of the findings and the likelihood of further development and
implementation.
Translation of the research findings into practice will yield products and interventions to
benefit both civilian and military populations challenged by disasters and emergencies of natural,
accidental, and intentional origin.
TABLE
Table 1: List of Research Projects
09 01 F
Biomarkers of Exposure to Chemical Terrorism Agents (Jeffrey D. Laskin, Ph.D.)
Abstract: This study focuses on developing biomarkers for sulfur mustard and related agents. The
project determines whether vesicant chemical terrorism agents of related chemistry from different
sources yield different signatures when reacted with a surrogate of an experimentally established
target of the agents. This research provides an understanding of how the reactivity of various
mustards will vary as a function of contamination with breakdown products. The project identifies
the identity of targets for vesicants on sensitive proteins that can be used as biomarkers of exposure,
compares different vesicants on the same target, and constructs a model on how vesicants bind to
specific proteins. Unique alkylation profiles can be used to determine the identity, quality, and
source of chemical terrorism agents.
09 02F
Interpretation of Prospective Exposure Studies Completed in New York City for Community
Catastrophic Planning (Sastry S. Isukapalli, Ph.D.)
Abstract: This study focuses on utilizing exposure concentration results from the New York urban
dispersion program (UDP) to establish the spatial and temporal patterns of impact from exposure to
highly toxic substances on members of the general public in New York City. Exposures using
typical human activity patterns are studied. UDP Data are interpreted in relation to information used
currently by community planners and emergency responders within urban settings. The agentspecific analysis results are employed to augment or provide suggestions for adjustments to existing
guidelines on evacuation, sheltering in place, transportation, and location of risk zone perimeters
after hazardous events. The study also incorporates estimates of the magnitude and severity of
casualties obtained for each toxic agent into a framework for characterizing stresses on health care
resources and are used to improve the strategy for the spatial location of passive samplers in New
York City and other locations that can be deployed after an event to determine the residual
contamination from non-volatile agents.
09 03F
Role Adherence Versus Role Abandonment in Disasters: Determinants of Response Personnel
Availability and Willingness to Perform (Clifton R. Lacy, M.D.)
In
Progress
Abstract: This study characterizes the likelihood of availability and willingness to perform their
duties and the effect on role adherence versus role abandonment in response personnel of various
disciplines during different disasters and health emergencies. The project also characterize the
determinants of availability and willingness to perform and the impact of mitigating factors on
improving availability and willingness to perform. The study characterizes and compare the
differences in responses of personnel of various disciplines.
8
09 04F
Emergency Shelter Location and Resource Allocation (Nina H. Fefferman, Ph.D.)
Abstract: This study develops optimization and simulation models to examine the optimal location
of emergency shelters and allocation of resources into the shelters and area hospitals to minimize
access time and maximize quality of service in responding to needs of individuals. The proposal
builds additions to the models to incorporate the potential for dynamic healthcare worker allocation.
This project yields active research-ware which will be able to provide a robust set of shelter
locations and evacuation routes for use in emergencies within specified study areas.
09 05F
Supply Chain of Critical Medical Resources for Emergency Situations (Tayfur Altiok, Ph.D.)
Abstract: This study evaluates supply chain activities of critical medical resources to be used by
hospitals in emergency incidents. The study focuses on the understanding of demand patterns for
critical medical resources and assessing optimal stockpiling and ordering policies. The project
utilizes a simulation-based approach considering a number of random parameters impacting
inventories of key drugs and equipment during emergencies. Based on demand patterns from
historical data, new inventory management strategies are developed for effectively meeting
hospitals' increased demand under emergency scenarios. A set of computational models are
developed to assist hospital administrators in making informed decisions about supply chain.
09 06F
Patient Flow Optimization under Regular and Emergency Hospital Operations (Mohsen A. Jafari,
Ph.D.)
Abstract: This study valuates techniques to optimize patient flow during regular and emergency
hospital operations. The project takes a logistical view of patient flow. The study focuses on systemwide macro analysis of underlying processes that constitute the elements of patient flow in the
emergency room and its surrounding operations. The study uses quantitative metrics to measure
effectiveness and builds macro level simulations for patient flow under both normal and emergency
situations.
09 07F
Comparison of Turnaround Times of Point-of-Care Testing and Laboratory-Based Testing for
Patients in the Emergency Department: Considerations for Use in Disaster Surge and Mass Casualty
In
Situations (Robert Eisenstein, M.D.)
Progress
Abstract: This study evaluates the use of Point-of-Care blood testing to avoid delays in patient
management, improve patient turnaround time, and enhance throughput in the Emergency
Department for routine care as well as during times of increased patient surge.
09 08F
Use of Ultrasound in the Emergency Setting to Improve Triage of Trauma Patients (Rajesh Geria,
M.D.)
Abstract: This study evaluates the use of portable ultrasound in the prehospital setting. Ultrasound
is used to rapidly determine presence or absence of pneumothorax, blood in the abdominal cavity,
and appropriate endotracheal tube placement. This project determines how this information,
obtained noninvasively in the prehospital setting, is used to improve patient care and to determine
the most appropriate facility to which to send the trauma patient.
09 09F
Use of Optical Scanning Devices to Improve the Speed of Emergency Patient Registration (Rajiv
Arya, M.D.)
In
Progress
Abstract: This study evaluates the use of optical scanning to improve speed and accuracy of data
collection for emergency department patient registration during daily routine and during periods of
increased patient surge. Speed and accuracy of registration are compared pre-and post
implementation of this new technology. This capability is of additional value in patient tracking
during mass casualty events.
09 10 P
Blast Resistant Glass Panels Using Thin Films for Protection of Emergency Vehicles
(Perumalsamy N. Balaguru, Ph.D.)
Abstract: This pilot study evaluates the levels of protection conferred by use of thin films to reduce
blast-related fragmentation and shattering of glass panels. The project evaluates the level of
protection provided by the film and the method to secure the film and the glass to the attached
frame.
09 11 P
Assessing the Economic Benefits of Public Health Mitigation and Resilience Measures (Michael
Greenberg, Ph.D.)
Abstract: This study assesses the capacity to build low-cost regional economic models to evaluate
the economic impacts of disasters and emergencies with and without both pre- and post-event
interventions, with special focus on public health. The study examines and tests the feasibility of
building and using a mathematical economic model that will enable ready assessments of the
relative performance of a stressed economy. The study also addresses whether the costs of these
models are offset by the potential value of their use in actual planning for catastrophic events,
especially those related to public health.
09 12 P
Intelligent Demand Assigned Networks Cost and Performance (Mohsen Garabaglu, Ph.D.)
Abstract: This study evaluates the use of intelligent demand assigned networks with shared space
segment environments in reducing the cost of the satellite-based Disaster Recovery and Emergency
Management networks used during catastrophic events. System architecture are developed for an
intelligent demand assigned network with the capability of sharing network resources among
multiple entities. Comparative business models associated with both typical satellite networks and
intelligent demand assigned networks are developed. Intelligent demand assigned networks and
typical satellite Disaster Recovery and Emergency Management networks are compared based on
performance and cost savings.
09 13 F
Bridging the Gaps between Public Health, the Health Care System, and First Responders (George
DiFerdinando, M.D., MPH)
Abstract: This project involves conducting and assessing education and training sessions pertaining
to all hazards health threats resulting in mass casualties. Training sessions target diverse
multidisciplinary elements of disaster preparedness and emergency response in counties or
multicounty regions of the state. Training and evaluation are performed in conjunction with federal,
state, county, and local partners.
10
CHAPTER 1
Project 09 OIF: Biomarkers of Exposure to Chemical Terrorism Agents
11
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Biotnarkers of Exposure to Chemical Terrorism Agents
Project 09 01 F
Final Report
Investigators: Dr. Jeffrey D. Laskin,Ph.D., UMDNJ-RWJMS. Environmental and Occupational
Health Sciences Institute, 170 Frelinghuysen Road, Piscataway, NJ 08854;
Email: jlaskin@eohsi.rutgers.edu
Panos Georgopoulos at UMDNJ-RWJMS
12
Abstract
Chemical weapons remain a threat to the military as well as civilian populations.
Mustard alkylating agents remain an agent of concern because of their toxicity, persistence in the
environment, difficulty in treating exposures, and lack of medical countermeasures. Sulfur
mustard is inexpensive and relatively easy to manufacture, it is also available from unaccounted
for munitions or disposal sites. Work in the present studies analyzed alkylation profiles of
mustard-class vesicants and yielded unique signatures when reacted with sensitive proteins so
that the agents may be differentiated from one another based upon reactivity. Using techniques
in analytical chemistry we also examined the possibility that breakdown products of vesicants
can modify target proteins. Unique profiles of modified targets were determined.
13
Foreword
This project was performed by Dr. Jeffrey D. Laskin and Dr. Panos Georgopoulos and
was sponsored by the University Center for Disaster Preparedness and Emergency Response
(UCDPER) - A Collaborative Initiative of Rutgers, The State University of New Jersey,
UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson University Hospitalwith support from Department of Defense Grant No. W9132T-10-1-0001.
We thank Yi-Hua Jan for expert technical assistance.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
14
Table of Contents
INTRODUCTION
15
Background
16
Objective
16
Approach
17
Methods
17
CONCLUDING CHAPTER
17
Findings
17
Reports and Manuscripts
19
Grant Proposals
21
Discussion
21
REFERENCES
21
15
INTRODUCTION
Background
There remains a major public health concern of exposure to toxic chemicals in a terrorist
attack. Chemical threats include toxins, toxic industrial chemicals as well as chemical warfare
agents. Higher priority chemical threats include vesicating agents such as sulfur mustard,
neurotoxic agents such as organophosphorus nerve "gases," pulmonary agents such as chlorine
gas and metabolic/cellular poisons such as cyanide. In an emergency situation, it is critical to
know the nature of the chemical an individual has been exposed to and at what levels. This
information is important for patient evaluation and for determining appropriate medical
treatments and strategies for decontamination. Our plans are to focus on developing biomarkers
for sulfur mustard and related agents. These compounds are potent alkylating agents. By
determining the alkylation signatures of these vesicants on target proteins, we can gain
knowledge as to how these toxins function and identify biomarkers of exposure. These
biomarkers can be used by medical personnel to rapidly identify the nature of chemical exposure
in an emergency situation.
Objective
The purpose of this proposal was to determine whether vesicant chemical terrorism
agents of related chemistry from different sources yield different signatures when reacted with a
surrogate of an experimentally established target of the agents. We hypothesize that the reaction
of the model sulfur mustard vesicant 2-chloroethyl sulfide or a related analog mechlorethamine
(HN2) with a likely naturally occurring substrate, thioredoxin reductase (Holmgren and Lu,
2010) or related antioxidants such as superoxide dismutase, will produce unique signatures when
analyzed for adduct formation. In addition, we hypothesized that when mixed in varying
16
proportions with naturally occurring sulfur mustard breakdown products, differentiate
alkylation signatures will be produced, based upon the relative amounts of these contaminants
present during the alkylation reaction. These products include thiodiglycol, which under certain
conditions react with mustards to generate sulfonium salts, and related sulfoxide and sulfone
derivatives which are oxidation products of the active principals. Little is known with regard to
the reactivity of these products, alone or in combination with the vesicants, much less toward a
likely protein substrate of the vesicant. Our research has provided a better understanding of how
the reactivity of various mustards will vary as a function of contamination with breakdown
products. They will also provide a set of biomarkers to establish exposure to vesicants.
Approach
Our approach was to analyze alkylation of target proteins by vesicants and to determine if
alkylation signatures on target proteins are modified by contamination products found in vesicant
preparations.
Methods
Proteins including thioredoxin reductase and superoxide dismutase were analyzed in cells
by western blotting to determine if they were modified by nitrogen mustard. Using purified
enzymes, proteins were analyzed using techniques in liquid chromatography mass spectroscopy
to identify alkylation signatures.
CONCLUDING CHAPTER
Findings
Oxidative stress plays a critical role in the toxicity of the nitrogen mustard bis(2chlorethyl) amine (HN2). The thioredoxin system, which consists of thioredoxin reductase
(TrxR), thioredoxin, and NADPH, is a key cellular antioxidant that is important in redox
17
regulation and protection against oxidative stress. HN2 contains two electrophilic chloroethyl
side chains that can react with nucleophilic amino acids in proteins leading to changes in their
structure and function. Previously, we reported that the monofunctionl vesicant 2-chloroethyl
ethyl sulfide targets TrxR by alkylating selenocysteine in the C-terminal redox motif of the
enzyme, a process leading to enzyme inhibition. In the present studies, we found that HN2
inhibits the thioredoxin system in A549 lung epithelial cells and in purified enzymes. Western
blot analysis revealed marked decreases in the TrxR monomer and increases in TrxR dimer and
oligmer formation indicating that HN2 cross-linked the enzyme. With the purified enzyme,
NADPH reduced, but not oxidized TrxR, was inhibited and cross-linked by HN2. Using biotinconjugated iodoacetamide (BIAM), which selectively reacts with selenol or thiol groups on
proteins, HN2 was found to decrease BIAM-labeled TrxR, suggesting that HN2 inactivates TrxR
by targeting critical selenol and/or thiol groups on TrxR. LC-MS/MS analysis confirmed that
HN2 directly adducted to the cysteine- and selenocysteine-containing redox centers forming
monoadducts, intra-molecule and inter-molecule cross-links, leading to enzyme inhibition. HN2
also cross-linked and inhibited dose-dependently on thioredoxin. LC-MS/MS analysis
demonstrated that HN2 alkylated the cysteine residues in the redox center of thioredoxin, leading
to enzyme inactivation and oligomerization. Disruption of the thioredoxin system is likely to
contribute to HN2-induced oxidative stress and cytotoxicity.
We also found that HN2 targeted the antioxidant superoxide dismutase. Like thioredoxin
reductase, HN2 was found to cross-link the protein in target cells. LC-MS/MS analysis
identified the cross links at the interface of superoxide dismutase dimers on cysteine residues.
Based on this work, two proteins, thioredoxin reductase and superoxide dismutase were
identified as important biomarkers of exposure to vesicants. These studies should provide an
18
initial framework for understanding the mechanism of action of vesicants, they will also provide
important sites for assessing exposure to an important chemical threat agent.
Reports and Manuscripts
a.
Studies on thioredoxin reductase as a biomarker of vesicant exposure
Jan, YH, Heck, DE, Laskin DL and Laskin JD, Selective cross-linking of thioredoxin reductase
in lung epithelial cells by nitrogen mustard, a model sulfur mustard vesicant, manuscript in
preparation
Oxidative stress plays a critical role in sulfur mustard-induced toxicity. The thioredoxin
system, which consists of thioredoxin reductase (TrxR), thioredoxin, and NADPH, is a critical
cellular antioxidant that is important in redox regulation and protection against oxidative stress.
Nitrogen mustards, including mechlorethamine (HN2), contain two electrophilic chloroethyl side
chains which can readily react with nucleophilic amino acids in proteins, a process that can lead
to changes in protein structure and/or function. Previously, we reported that the monofunctionl
vesicant 2-chloroethyl ethyl sulfide targets TrxR by selectively alkylating selenocysteine in the
C-terminal redox motif of the enzyme, a process leading to enzyme inhibition. In the present
studies, we evaluated the effect of HN2 on the thioredoxin system using A549 lung epithelial
cells and purified TrxR. HN2 was found to cause a concentration-dependent (1-100 uM)
inhibition of TrxR in both systems. Western blot analysis revealed decreases in the TrxR
monomer and simultaneous increases in TrxR dimer formation. Using biotin-conjugated
iodoacetamide (BIAM) to selectively react with low pKa selenol or thiol groups on proteins at
pH 6.5, we found that HN2 differentially decreased BIAM-labeled TrxR in A549 cells and with
purified enzyme, suggesting a decrease in the reduced form of TrxR. These results suggest that
HN2 inactivates TrxR by targeting selenol and/or thiol containing redox centers and cross-
19
linking TrxR peptides. Disruption of the Trx system is likely to contribute to vesicant-induced
cytotoxicity.
b. Studies on cross-linking of superoxide dismutase as a biomarker of vesicant exposure
Y. Wang; D. E. Heck; D. L. Laskin; J. D. Laskin (2011) Mechanisms of vesicant-induced
cytotoxicity in lung epithelial cells, report presented at the Society of Toxicology Annual
Meeting, Washington, DC.
Inhalation of vesicants such as sulfur mustard can cause significant damage to the respiratory
system including inflammation, upper and lower obstructive disease, and acute respiratory
distress syndrome. A major factor contributing to vesicant-induced lung injury is cytotoxicity
and oxidative stress. In the present studies, we used nitrogen mustard (NM, mechlorethamine), a
bifunctional alkylating agent and model sulfur mustard vesicant, to characterize cytotoxicity and
oxidative stress in A549 cells, a human type II lung epithelial cell line. NM was found to cause a
concentration-dependent inhibition of A549 cell growth (IC50 = 1 uM). Pretreatment of the cells
with 20 uM buthionine [S, R] sulfoximine (BSO) for 6 hr, which depletes glutathione (GSH),
was found to enhance NM-induced growth inhibition (IC50 = 0.2 uM). Cell cycle analysis
revealed that 27.1 ± 0.7 % of A549 cells were in the S phase, 18.0 ±1.0 in G2M and 53.4 ± 0.5%
in GoGl. Twenty-four hr after treatment of the cells with NM (30 uM, 30 min), we observed a Sphase block, 64.6 ± 1.4% of the cells were in S phase, 25.2 ± 1.5% in G2M and 9.3 ± 0.4 in
GoGl. Depletion of GSH in cells had no effect on NM-induced cell cycle arrest. NM (30-300
uM) also enhanced the generation of intracellular hydrogen peroxide, as determined by flow
cytometry in conjunction with the hydroperoxy-sensitive probe 2',7'-dichlorofluorescein.
Western blotting showed that while NM had no effect on expression of the antioxidant enzymes
catalase or heme oxygenase-1; it cross-linked superoxide dismutase, forming a modified 32,000
20
molecular weight homodimeric protein. Taken together, these data indicate that NM induced
cytotoxicity in lung epithelial cells is associated with oxidative stress and alterations in
antioxidants, processes that can contribute to vesicant-induced tissue injury.
Grant Proposals
NIH grants on the mechanisms by which nitrogen mustard modified antioxidant proteins
are in preparation.
Discussion
Sulfur mustard and related agents cause epithelial disruption and oxidative stress.
Oxidoreductases are important enzymes mediating these processes. We have determined that
thioredoxin reductase and superoxide dismutase are important targets for these compounds.
Mustard alkylation sites were analyzed using techniques in limited protease digestion of the
modified proteins, peptide purification by sodium dodecylsulfate polyacrylamide gel
electrophoresis and sequence analysis by liquid chromatography (LC) tandem mass
spectrometric (MS) analysis. For the ethylthioethyl-alkylated protein products, samples were
first reduced with dithiothreitol, alkylated with iodoacetamide, and subjected to in-gel digestion
with the proteinase Lys-C, prior to LC-MS/MS. Alkylation signatures of mustard derivatives
were compared to determine their unique reactivity features. Based on this work, both
thioredoxin reductase and superoxide dismutase have the potential to serve as biomarkers for
exposure to vesicants.
REFERENCES
Holmgren, A. and J. Lu. 2010. "Thioredoxin and thioredoxin reductase: current research with
special reference to human disease." Biochem Biophys Res Commun. 396(1): 120-124.
21
CHAPTER 2
Project 09 02 F: Interpretation of Prospective Exposure Studies Completed in New York
City for Community Catastrophic Planning
22
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Interpretation of Prospective Exposure Studies Completed in New York City for
Community Catastrophic Planning
Project 09 02 F
Final Report
Investigators: Sastry S. Isukapalli, Ph.D., Environmental and Occupational Health Sciences
Institute (EOHSI), 170 Frelinghuysen Rd, Piscataway, NJ 08854; Phone: 732445-0171;
Emai 1: sastry@turandot. rutgers. edu
Paul J. Lioy, Ph.D., UMDNJ-RWJMS
23
Abstract
This project utilizes the unique data set of actual human exposure and contaminant
concentration measurements under realistic conditions tracer concentration and exposure data
from the NY Urban Disperson Program (UDP) experiments, in order to improve upon our
current understanding of the impact of emergency events on the general public and
corresponding community planning efforts. The experimental data on outdoor, indoor, and
personal exposure concentrations following releases of small amounts of inert tracers are scaled
in order to characterize exposures that people would be experiencing in the event of releases of
chemical, biological, or radiological agents. Novel methods have been developed for (a) scaling
up tracer concentrations to specific CBR release scenarios, (b) specifying plausible population
distributions in a Geographic Information Systems (GIS) setting, (c) estimating casualties and
stress on health-care resources under different event and potential response scenarios.
Demonstration case studies highlight the major factors that must be accounted for in
emergency response studies, and the overall uncertainty in casualty estimates due to uncertainties
in release location and release timing. The computational modules and algorithms presented here
can provide actionable and accurate information to personnel responding to emergency events in
complex urban settings.
24
Foreword
This project was performed by Dr. Sastry S. Isukapalli and Dr. Paul Lioy and was
sponsored by the University Center for Disaster Preparedness and Emergency Response
(UCDPER) - A Collaborative Initiative of Rutgers, The State University of New Jersey,
UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson University Hospitalwith support from Department of Defense Grant No. W9132T-10-1-0001.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
25
Table of Contents
Abstract
24
Foreword
25
INTRODUCTION
27
Background
27
Objective
28
29
Specific Aims
Approach
29
Scope
36
Mode of Technology Transfer
38
CONCLUDING CHAPTER
38
Summary
38
Conclusions
39
Recommendations
39
REFERENCES
41
26
INTRODUCTION
This project utilizes the experimental data conducted during the NY Urban Disperson Program
(UDP) experiments, which involved releases of small amounts of inert tracers and subsequent
measurements of outdoor, indoor, and personal exposure concentrations. These data represent
actual exposure concentration profiles and provide comprehensive dispersion/exposure
measurements in real-life urban settings. Since these tracers were released from different
locations and times to characterize multiple space and time profiles under the same conditions,
and measured in different "receptor locations" representing exposures to personnel working near
the release location, workers and general public passing through the area, and tourists that
wander in multiple locations.
Background
Atmospheric dispersion of chemicals in urban settings is complicated by multiple factors such as
irregular structures, street canyons, building ventilation characteristics, traffic, etc. In the recent
past, the New York City Urban Dispersion Project (UDP- Lioy et al., 2007; Watson et al., 2006)
was conducted as a collaborative effort involving multiple agencies such as the Department of
Homeland Security, USEPA, NOAA, and DARPA, as well as the City of New York. The UDP
experiments involved releases of very low levels of harmless perfluorocarbon tracers (PFT) that
can be detected at very low levels and are used in leak detections, atmospheric tracking, and and
building ventilation (Watson et al., 2006; NOAA, 2011). These were released in Midtown
Manhattan at separate locations, during two seasons in 2005, and focused on measuring
concentrations following both outdoor and indoor releases of the tracers. Within this project,
prospective exposure tracer experiments were completed at the Madison Square Garden (MSG),
at the Rockefeller Center (RC), and in the New York City subway system; these experiments
27
involved realistic scripted activities of individuals for characterizing exposures that people would
be experiencing in the event of releases of chemical, biological, or radiological agents. The
concentration and exposure data obtained during the NY UDP lend themselves to easy scale up
for other types of chemicals, and evaluation of computer simulation models. In some exposure
scenarios, the concentrations stayed at higher levels due to "secondary PFT releases" from
buildings that accumulated the contaminants during the period of release or that re-emitted the
PFTs to the outdoor environment after the release stopped. Measurements of neighborhood scale
PFT concentrations (up to distances of several blocks away from the release points) can provide
information needed to establish a baseline for determining how different types of releases could
affect exposures both to the general public and to emergency responders.
It must be noted that the NY UPD data represent a unique set of actual human exposure
measurements under realistic conditions, and are not outputs from a computer model. Therefore,
uncertainties associated with realistically representing emergency response situations (e.g. due to
simplifications in model formulations) are reduced, and the results directly applicable to
community emergency response planning process for NYC, and can be interpreted to help
planners elsewhere.
Objective
The overall objective of the project is to improve the current understanding of the impact of
emergency events on the general public and improve community planning efforts, by utilizing
the realistic human exposure data available from the UDP study to increase awareness at
multiple levels of emergency response on the health impacts of potential releases of highly toxic
chemical, physical or biological agents in urban centers like New York City. This information
can be interpreted in current planning approaches for community response and then be made
28
available to planners in the federal government, the military, and the public improve or augment
applicable community procedures for catastrophic events.
Specific Aims
This specific aims of this project were to:
1. Utilize the exposure concentration results from the NY Urban Dispersion Program to:
establish the spatial and temporal patterns of impact from exposure to highly toxic substances
on members of the general public in NYC, including typical activity patterns, and interpret
them in relation to information used currently by the community planners and emergency
responders within urban settings. The results and their visualization will provide realistic
estimates of the contact that would have occurred if the tracers had been actual CBR agents.
These transformed results provide information for assessing potential exposures and risks
within a community from releases of selected highly toxic substances.
2. Employ agent-specific analyses of UDP data to augment and/or provide appropriate
suggestions for adjustments to: existing guidelines on evacuation, shelter in place,
transportation routes, and location of "cold zone" perimeters after hazardous events.
3. Incorporate estimates of the magnitude and severity of casualties obtained for each toxic
agent into a framework for characterizing stresses on health care resources by: realistically
estimating casualties (response personnel and general community) from a catastrophic
release.
4. Interpret the agent-specific analyses for improving strategies for the spatial location of
passive samplers in NYC and other locations that can be easily deployed after an event to
determine the residual contamination from non-volatile agents.
Approach
29
This project follows an approach that will allow rapid analysis of casualties and stress on health
care scenarios in a Geographic Information Systems (GIS) framework. Specifically it provides a
procedure for performing GIS-based analysis for a diverse set of agent releases (chemical,
biological, and radiological) and exposure scenarios provided as inputs to the system. It allows
for estimating the overall impacts in terms of both casualties and in terms of stress on health care
resources.
Selection of representative CBR agents:
The following agents were selected after consultations with project collaborators and agencies
related to emergency response to study the following plausible emergency event scenarios: such
as accidents involving transportation of hazardous material, and terrorist events involving a
chemical warfare, biological, or radiological agents (CBR agents). Example CBR agents studied
using this system include: (a) chlorine and phosgene representing industrial chemicals,
(b) anthrax representing a biological warfare agent, (c) sarin representing a chemical warfare
agent, and (d) cesium representing a radiological agent.
The interpretation of exposure data was performed by utilizing the Acute Exposure Guideline
Levels (AGELs) as metrics relevant for exposures of general populations and emergency
responders. In general, measures for response to community catastrophes as well as for disaster
control can be projected more accurately on the basis of the AEGL framework (NRC, 2001).
AEGL values represent toxicologically substantiated ceiling exposure levels for different
relevant exposure periods (10 minutes, 30 minutes, 1 hour, 4 hours, 8 hours), with AEGL-1
denoting the threshold for notable discomfort; AEGL-2 denoting the threshold for serious, longlasting effects or an impaired ability to escape; and AEGL-3 denoting the threshold for lethal
effects. In case of agents such as anthrax, the corresponding analyses were made based on lethal
30
doses (e.g. LDio indicating that 10% of exposed population will be infected). These different
metrics are useful in assisting the responders and planners in defining ways to address specific
situations.
Interpreting the UDP data to plausible scenarios within the New York City:
The UDP data were translated to multiple CBR agents and applied to different scenarios within
Midtown Manhattan. For example, the Madison Square Garden has a capacity of approximately
18,000 to 20,000 depending on the type of event. The Rockefeller Center, correspondingly, has
about 200,000 to 350,000 visitors per day, translating to about 10,000 to 70,000 people in the
area depending on the time of the day. A background population density of 27,500/km2 was also
used to represent residential population in this region. Computer modules have been developed
to represent and analyze, within a GIS system, these population distributions in relation to
potential threat zones after the release of a CBR agent. Sensitivity analyses were performed
considering differential impacts of emergency events based on different potential occupancy
levels, distribution of people around the location based on different time periods in relation to an
event. This approach will be applicable to many other urban areas that share similar geographic
attributes as areas studied in the NY UDP experiment.
Characterizing the zones of population distributions:
In order to assess the impact of releases of CBR agents on the general populations, estimates of
number of people (event attendees and general public in the area) are required in conjunction
with information on the spread of contaminant plumes or threat zones. These zones are
developed based on anticipated population distributions during an event at the MGS or during a
specific time of day at the Rockefeller Center. In the case of the MSG, the zones include an inner
most zone (Zone 1) representing the MSG arena (with a total seating capacity of 18,000 to
31
20,000). Zone 2 represents the immediate vicinity (reflecting people walking into or out of the
arena), Zone 3 represents nearby public (e.g. reflecting attendees that are making way to the
event). Zone 4 reflects an expansion of Zone 3 (accounting for attendees walking from bus or
train stations to the arena). Zone 5 reflects the general "background population" in the city, and
covers the entire study area for impact analysis (a background population density of 27,500/km2
is assumed).
An emergency planner can specify how the overall attendees are distributed within each zone
and can also specify any changes to the background population distribution due to the
performance event. It must be noted that the extent of the plume spread cannot be extrapolated to
larger areas than the measured distances in the original UDP experiment because the accuracy of
such extrapolations reduces with increasing distances away from translated measurement
locations.
Assessing the spread of contaminants across different threat zones:
The UDP data have been translated to Chlorine, Sarin, Phosgene, Cesium, and Anthrax, and
focus on a "medium" scale warfare agent release or a transportation incident (e.g. 1 ton/hr
release of chlorine from a truck; 100 kg/hr release of Sarin from a small truck; 1 ton/hr release of
phosgene from a small truck; and 100 g/hr release of anthrax spores from a small bag).
The nature of the UDP data allows us to couple information on source characteristics and
exposure measurements along with the chemical and physical characteristics of the tracer and an
agent of concern in such a way as to obtain estimates of ambient concentrations and exposures to
different chemical, radiological and biological agents by applying "realistic release scenarios."
However, the approach still approximates the properties of the agents using an assumption of an
ideal gas. Specifically, for each agent of concern, the agent is assumed to disperse in a manner
32
similar to an ideal gas. The concentration estimates for each hypothetical release scenario are
then based on scaling the source terms used in each UDP experiment, along with the properties
of the agent of concern, and the source strength in the scenario. These scaled concentrations are
then used to develop plume profiles and "threat zones" based on Acute Exposure Guideline
Levels. In order to estimate potential stresses on the health care resources, these plume snapshots
are integrated with spatial distributions of populations.
Specifying potential exposure scenarios for assessing impacts and casualties:
The approach and modules developed in this project for assessing risks associated with different
hypothetical emergency events are formulated in terms of parameters that are tangible for an
emergency responder or planner. These parameters are intended to accurately characterize
potential population distributions, and are described in the following:
Release Location: The release location can be specified to correspond to one of the multiple
different locations of tracer releases in the UDP experiment. For example, in case of the MSG
experiment, valid options are release points A, B, C, and D, corresponding to the north west,
north east, south east, and south west corners, of the MSG.
Type ofAgent Released: This project focused on the following chemical, biological, and
radiological warfare agents for exposure and risk estimation: chlorine, sarin, phosgene, fuel oil,
cesium, and anthrax. The scenarios focused on "medium" scale warfare agent releases and
transportation incidents, as described earlier.
Occupancy Percentage in the Arena: The analysis focused on different levels and types of
population distributions within and around the arena: inside the arena (Zone 1), immediate
vicinity (Zone 2), nearby public (attendees and general public; Zone 3), additional event-related
people (Zone 4), and the general background population in the city (Zone 5). Different
33
hypothetical occupancy levels and population distributions were specified by using numbers for
percentages of the event population within each zone. It must be noted that the event related
population is partitioned into Zones 1 through 4, with 100% indicating full attendance.
Based on this approach, the following examples illustrate the types of scenarios that can be
specified:
1.
[Zone 1: 20%, Zone 2: 20%, Zone 3: 40%, Zone 4: 20%] indicates that the event is
completely sold out (sum of percentages adding up to 100% of the arena capacity) and with the
distribution of the population mostly in the area around the arena.
2.
[Zone 1: 100%, Zone 2: 0%, Zone 3: 0%, Zone 4: 0%] indicates that the event is
completely sold out and all attendees are inside the arena.
3.
[Zone 1: 40%, Zone 2: 0%, Zone 3: 0%, Zone 4: 0%] indicates that the event is not fully
sold out (only 40% sold) and all attendees are inside the arena.
4.
[Zone 1: 0%, Zone 2: 0%, Zone 3: 0%, Zone 4: 0%] indicates that the incident is
specified for a time when there is no ongoing event taking place.
Similar distributions can be specified to address additional scenarios.
Background Population Distribution (relative to residential population distribution): This
specifies the increase or decrease in the population levels nearby (e.g. increased number of
people due to commuting during weekdays, decrease in the number of people during weekends,
etc.) The baseline density of the population is set to the average population density in the city
(27,500/km2). Increases and decreases can be specified by relative percentages, with 100%
corresponding to no change in population density.
Indoor Penetration of Contaminants: This corresponds to the relative levels of an agent indoors
based on estimates of outdoor levels and utilizes a simplified approximation of the factors
34
affecting the penetration of outdoor contaminants indoors. Specifically, a linear ratio of outdoor
to indoor levels has been used to estimate potential impacts and risks. These ratios are directly
applied to the estimates of corresponding outdoor risks to estimate indoor risks. For example, if
the level outdoors is at AEGL-3, and the indoor ratio is 0.1, this approach estimates that 10% of
the people indoors will be at risk level corresponding to AEGL-3. This assumption is not always
applicable, but it has been used to account for the heterogeneity within the indoor environment
and to account for the incomplete understanding of complex processes governing indoor and
outdoor levels.
Estimated Risks: These are based on spatial multiplications of the maps of the population
distribution fields with the spatial fields reflecting different risk levels, and the results are
summarized in terms of as numbers of people that suffer (a) serious injury, (b) non-serious
injury, and (c) mild inconvenience. The AEGL levels for individual chemicals have been used to
characterize the threat levels.
Impact on Available Health Care Resources: Information on available hospitals and emergency
care facilities near the vicinity of the arena has been used to estimate the potential impacts on the
health care system. The major attributes considered in this analysis include (a) capacity of the
facility (in terms of available number of beds), (b) distance to the facility, and (c) an analystspecified "preference" of the health care facility. Examples of major hospitals near the MSG area
include: (1) Beth Israel Medical Center, with a capacity of approximately 1400 beds, at a
distance of 1.2 miles, (2) Bellevue Hospital Center, with a capacity of about 800 beds, at a
distance of 1.1 miles, (3) NYU Lagone Medical Center (Tisch Hospital) with a capacity of 700
beds, at a distance of 1.2 miles.
35
One of the default options in the computational modules is the assignment of individual weights
to each hospital based on the distance (an inverse distance metric using the nearest rounded
mile). The approach also provides an option to input the preference of the operator, based on
expert judgment and an understanding of the areas to be studied.
Scope
The approach of scaling the UDP measurements and the computational modules developed here
are intended to be a resource for emergency planners and responders, with an understanding that
it will supplement their wide range of experience and expertise with actionable information that
is based on new experimental data on contaminant dispersion and exposures to populations in
complex urban areas. The data interpreted in this study are based on tracer experiments that
consider real settings of urban geography, and movement of people in the vicinity of a release,
etc. Thus, the findings of this project are expected to aid the emergency planner and responder.
The computational modules allow an analyst an understanding of and actionable information on
the following:
1. Patterns threat zones in terms of severity of consequences following an emergency
incident: serious injury resulting in illness or death, moderate injury, minor effects, and
safe area. This will also provide information on areas emergency responders should not
enter, where they should use full protective equipment, and where they can enter even in
the absence of complete protective equipment. These will also help in identifying where
temporary treatment facilities should be set up (i.e. outside the lowest threat zone, but
with an adequate margin of safety).
36
2. Changes in the different threat zones changes based on the type of the agent involved in
the incident (i.e., to understand how the zones of impact change based on the toxicity of
the agent).
3. Casualties expected under different release conditions (timing and amount of releases),
focusing on various degrees of injury for the local population, workers, visitors, and
emergency responders.
4. Impact on the health care facilities in the vicinity, to understand the type of plans needed
to address the level of expansion required for available facilities or adjunct units (e.g.
mobile hospitals).
5. Uncertainty in estimates of threat zones and casualties. This allows for providing a degree
of confidence in different estimates, and what type of "margin of safety" should be
employed in preparedness, planning and response actions.
The results provide information useful in achieving the following community catastrophic
emergency preparedness and response goals:
1. Identifying the types of threat detection systems that need to be in place for different
threat scenarios. This is critical because for many event locations, currently there is a lack
of realistic estimates of potential human exposure and risks to attendees and local
population following a large-scale accident or a terrorist event.
2. Providing a resource for an in-service training system for emergency responders that
complements existing training material.
3. Provide a rational scientific basis for incorporating plume modeling and analysis
outcomes into an overall training system/functional field program.
37
4. The analysis of different scenarios and reports on case studies are expected to be useful
for soliciting feedback from specific stakeholders by providing information that would be
most valuable for the stakeholders.
Mode of Technology Transfer
Detailed descriptions of the algorithms, computer source code for the algorithms, GIS shape files
consisting of plume snapshots, population distributions, etc., will be provided as part of the final
report.
CONCLUDING CHAPTER
Summary
The computational modules and the approach described above have been used for assessing the
risks to attendees and to the general population at the Madison Square Garden, and the general
public and visitors at the Rockefeller Center. Different chemical and biological agents were
simulated and the estimates of casualties and the number of people to be admitted to the hospitals
were computed. Additionally, the uncertainty associated with the estimates of overall casualties
arising from the uncertainty in the release location has also been characterized by comparing the
estimates from different release location assumptions. This highlights the need for accurately
characterizing the local meteorology as well as the uncertainties in the release characteristics
(location and timing). Based on the analysis results, the Project Team has suggested
recommendations on the type of field studies that can be developed to ensure that effective
planning, preparedness, and response strategies can be developed using all the available
information. Additionally, these analyses can be extended for improving the mathematical
models for predicting potential acute human exposures to accidental or deliberate releases of
38
harmful gases Hanna & Baja, 2009, and for establishing more effective guidelines for emergency
response entrance and exit strategies and for estimating the location of potential victims.
Conclusions
The analyses and case studies provided here can be considered more as "demonstration case
studies" of a framework that will allow improvement of emergency planning and response. This
approach can be translated to other arenas or urban locations of interest and can be expanded to
other type of industrial chemicals, chemical warfare agents, non-infectious biological agents, and
radiological agents. The example agents selected for this study are representative of the suite of
agents that can be volatilized or released as fine particles into the atmosphere. It does not cover
the longer impact of reactive materials or materials which of very molecular weight that will
deposit and contaminate the ground or other surfaces (e.g. vesicants).
The project demonstrates the critical importance of the following factors in relation to potential
impacts: (a) timing of an emergency incident in relation to the progress of an event, (b) accurate
characterization of the release location (e.g. different corners of an arena), (c) ventilation
conditions in an arena, (d) dynamics of urban dispersion that cannot be adequately captured by
currently used simple dispersion models such as ALOHA (Areal Locations of Hazardous
Atmospheres; NOAA, 2004), and HPAC (Hazard Prediction and Assessment Capability; DTRA,
2003), and (e) the relative toxicities of individual chemicals (which has substantial impact on the
threat zones).
Recommendations
The following observations and recommendation can be made based on the analyses:
There is substantial sensitivity of casualty estimates to the time of the incident in relation to the
progress of an event at the arena, and location of release. This implies that multiple alternative
39
response strategies should be in place to address emergency events during different stages of an
event in an arena. Overall, a complete "toolbox" of measurement devices, dispersion models,
and visualizations based on data analysis of the type presented will be valuable to emergency
responders. These tool boxes will help an emergency planner/responder make decisions by
taking into account all available data, along with their experience and judgment, in order to
arrive at optimal approaches for responding to an emergency.
40
REFERENCES
Hanna, S., and Baja, E. 2009. A simple urban dispersion model tested with tracer data from
Oklahoma City and Manhattan. Atmospheric Environment 43 (4):778-786.
Lioy, P.J., Vallero, D., Foley, G., Georgopoulos, P., Heiser, J., Watson, T., Reynolds, M.,
Daloia, J., Tong, S., and Isukapalli, S. 2007. A personal exposure study employing
scripted activities and paths in conjunction with atmospheric releases of perfluorocarbon
tracers in Manhattan, New York. Journal of Exposure Science and Environmental
Epidemiology 17 (5):409-425.
NOAA. 2011. Air Resources Laboratory. Atmospheric tracer safety. National Oceanic and
Atmospheric Administration (NOAA) [2 March 2011]. Available from
http://www.noaa.inel. gov/capabi 1 ities/tracers/tracersafety.htm.
NRC. 2001. Standing Operating Procedures for Developing Acute Exposure Guideline Levels
for Hazardous Chemicals. Subcommittee on Acute Exposure Guideline Levels,
Committee on Toxicology, Board on Environmental Studies and Toxicology,
Commission on Life Sciences, National Research Council. Washington, D.C.
Watson, T.B., Heiser, J., Kalb, P., Dietz, R.N., Wilke, R., Weiser, R., and Vignato, G. 2006. The
New York City Urban Dispersion Program March 2005 Field Study: Tracer Methods and
Results (Report). Brookhaven National Laboratory. BNL-75552-2006.
41
CHAPTER 3
Project 09 04 F: Emergency Shelter Location and Resource Allocation
42
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Emergency Shelter Location and Resource Allocation
Project 09 04 F
Final Report
Investigator: Dr. Nina Fefferman, Department of Ecology, Evolution and Natural Resources
Rutgers, The State University.
Email: Fefferman@aesop.rutgers.edu
43
Abstract
Climate-related health emergencies are a considerable burden to public health
infrastructure. The objective of this project is evaluating quantitative strategies for the allocation
of evacuees, resources, and healthcare workers in response to a sustained heat wave in the
Newark, NJ area. Using an interdisciplinary team of researchers in climatology, operations
research and epidemiology, we explored dynamic response plans to address the urgent care needs
of vulnerable populations during heat events, as well as quantifying the timing and nature of
medical complications that stem from extreme temperatures.
The research has yielded promising empirical results, quantifying both the extent and
nature of heat waves and temperature spikes in the Newark area, and the consequences of heat
spikes on gastrointestinal illness in the elderly population of the United States. Additionally,
theoretical modeling studies building off these empirical results have shown that dynamic
routing strategies that direct patients, health-care workers and equipment to available healthcare
facilities and other shelters can have a direct and measurable impact to reduce excess mortality.
Lastly, simulation based studies have shown that simple and practical routing algorithms can be
successful in making progress toward theoretical optimal reduction in adverse health outcomes
from heat-related emergencies.
44
Foreword
This project was performed by Fefferman, Nina H., Baykal-Giirsoy, Melike., Boros,
Endre., Eisenstein, Robert., Carpenter, Tami., Roberts, Fred., Robinson, David., Naumova, Elena
N., and Chui, Kenneth and was sponsored by the University Center for Disaster Preparedness
and Emergency Response (UCDPER) - A Collaborative Initiative of Rutgers, The State
University of New Jersey, UMDNJ-Robert Wood Johnson Medical School and Robert Wood
Johnson University Hospital- with support from Department of Defense Grant No. W9132T-101-0001.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
45
Table of Contents
List of Figures
48
List of Tables
50
INTRODUCTION
51
Background
51
Objective
52
Approach
52
Scope
52
Mode of Technology Transfer
52
RESEARCH METHODS
53
Introduction
53
Climatology
53
Epidemiology
54
Operations Research
55
Agent-Based Simulations
57
RESULTS
59
Climatology
59
Epidemiology
60
Operations Research
61
Agent-Based Simulations
62
CONCLUSIONS AND RECOMMENDATIONS
63
Summary
63
Conclusions
63
46
Recommendations
63
REFERENCES
64
FIGURES
66
TABLES
83
REFERENCE DOCUMENTATION
86
47
List of Figures
1. Location of the Newark Liberty International Airport
66
2. Area of study for evacuation routing optimization model of the Newark area,
with potential evacuation sites and local zoning
67
3. Days spent at or above 90° per year from 1997 to 2010
68
4. Hourly observations at or above 90° per year from 1997 to 2010
68
5. Average days per year spent at or above 90° per year by hour, from 1997
to 2009
69
6. Days spent at or above 100° per year from 1997 to 2010
69
7. Hourly observations at or above 100° per year from 1997 to 2010
70
8. Distribution and timing of gastrointestinal hospitalization among U.S. elderly
in 122 cities
71
9. Magnitude of the estimated effect of extreme heat events on gastrointestinal
infections (GI) and unspecified gastrointestinal disease (GS)
72
10. Example modeled evacuation routing scheme to cooling and treatment centers
in the Newark area
73
11. Map displaying census blocks (with centers noted in red) in Newark, NJ
74
12. Extreme-Scenario Percent of Population suffering adverse reactions to heat
exposure overtime in Practical Routing Algorithms
75
13. Extreme-Scenario Percent of Population "Dead" over Time in Practical
Routing Algorithms
76
14. Extreme-Scenario Percent of Population Receiving Treatment over time in Practical
Routing Algorithms
77
48
15. Extreme-Scenario Percent of All Centers At or Exceeding Capacity in
Practical Routing Algorithms
78
16. Extreme-Scenario Number of Locations Blocked by Traffic Jams over Time
in the Practical Routing Algorithms
79
17. Extreme-Scenario Locations Where the Practical Routing Algorithms Predict
Clusters of Deaths When Routing According to the "Nearest" Algorithm
80
18. Extreme-Scenario Locations Where the Practical Routing Algorithms Predict
Clusters of Deaths When Routing According to the "Nearest Appropriate"
Algorithm
81
19. Extreme-Scenario Locations Where the Practical Routing Algorithms Predict
Clusters of Deaths When Routing According to the "Nearest Appropriate
Exact" Algorithm
82
49
List of Tables
1. Number of hospitalizations attributed to GI related illness in the U.S.
elderly aged 65 or over between 1997 and 2004
83
2. Number of excess casualties during a simulated heat-related
emergency in the Newark area, under increasing levels of evacuation shelter
coverage
83
3. Input Data for the Practical Routing Algorithms
84
4. Extreme-Scenario Results from "Nearest" Algorithm in Practical
Routing Algorithms
85
5. Extreme-Scenario Results from "Nearest Appropriate" Algorithm in Practical
Routing Algorithms
85
6. Extreme-Scenario Results from "Nearest Appropriate Exact"
Algorithm in Practical Routing Algorithms
86
7. Results from Example Resource-Allocation-to-Shelters Scenarios from
the OR optimization model
86
50
INTRODUCTION
Background
Extreme climatic events, including hurricanes, blizzards and heat waves, have substantial
health related consequences, including severe morbidity and mortality. The 1995 heat wave in
Chicago, for example, caused an estimated 696 excess deaths (Whitman et al. 1997). During
these events, with the prospect of failing heating or cooling systems, electrical grid failures etc.,
the elderly or other vulnerable populations may be evacuated to central locations for care. These
types of evacuations - and the underlying increase in healthcare needs - can create sudden
surges in demand for the healthcare system, and increase the complexity of disaster management
operations. These logistical consequences can be mitigated somewhat by careful planning of the
placement of evacuation shelters, emergency personnel and equipment, and evacuee routing
strategy.
Building on previous research conducted as a project of the Climate and Health Research
Initiative, supported by a grant from the Rutgers University Academic Excellence Fund (CHRI
AEF), we explore health-outcome sensitive strategies for evacuation and shelter placement in
cases with both assumed already-existing resources/capacities at hospitals and shelters, and then
expand our findings to consider a dynamic problem where healthcare workers and services may
be deployed in targeted areas to alleviate local shortages in essential personnel.
Our models allow for the handling of random capacity needs and demands, developed in
close collaboration with UMDNJ and RWJ University Hospital, as well as state-level emergency
management agencies. They were created and analyzed by the existing group of interdisciplinary
collaborators, including experts in climatology, spatial modeling, operations research,
epidemiology and industrial engineering. Our results are intended to help provide decision
51
makers in emergency response agencies with more robust planning strategies for climate-related
health emergencies, as well as establish a strong foundation for further research proposals.
Objective
This study will develop optimization and simulation models to examine the optimal
location of emergency shelters and allocation of resources into the shelters and area hospitals to
minimize access time and maximize quality of service in responding to needs of individuals.
The proposal will build additions to the models to incorporate the potential for dynamic
healthcare worker allocation. The project will yield active research-ware which will be able to
provide a robust set of shelter locations and evacuation routes for use in emergencies with
specified study areas.
Approach
For information on the research methodology, please refer to the Research Methods
chapter.
Scope
The reader should be aware that the results in this report are based heavily on parameter
values for the Newark, New Jersey area. While the methods themselves are generalizable, other
locations that do not share these parameters will not necessarily have the same findings.
Additionally, as with all models (both optimization and simulation), some simplifying
assumptions have been made, and they may not fully represent real situations. The results are
therefore meant only to explore the relative quality of different types of routing and allocation
decisions, and not to predict the actual numbers of adverse health outcomes expected or
mitigated by intervention during heat events.
Mode of Technology Transfer
52
Based on our research, our recommendation is to base actions/planning for the mitigation
of adverse health outcomes from extreme heat events on a combination of theoretical
optimization studies which will determine best placement of temporary health-care facility
locations and resource allocation to those locations, and simulation experiments which will help
determine which instructions/announcements to the public will help achieve best results for
understanding, compliance, and overall health outcomes.
RESEARCH METHODS
Introduction
The design of optimal evacuation strategies for a climate-related emergency can be
approached using a wide variety of methods from different fields. We use four primary
techniques, rooted in Epidemiology, Climatology, Operations Research, and Agent-Based
Simulation. The first two form the bulk of the task of parameterizing the models, providing
empirically grounded data from which to build scenarios. The former uses well-established
techniques to find solutions to challenging logistical problems - in this case, dynamic evacuation
and emergency management routing during a climate-related health emergency. The present
research focuses on a particular scenario, an extreme heat wave in the Newark area.
Climatology
Dr. David A. Robinson, New Jersey State Climatologist and Chair of the Rutgers
University Department of Geography directed the climatology portion of the research. The
primary aim of the climatology portion of the project was to characterize the levels of excessive
heat in Newark, NJ during recent years - 1997 to 2010, in order to provide realistic heat wave
scenarios for the given environment.
53
Temperature measurements over a 14-year period were collected at a National Weather
Service Automated Surface Observing Station (NWS ASOS) situated between the outermost
runaway of Newark Liberty Airport and the New Jersey Turnpike (Figure 1). This location is
situated between downtown Newark and Elizabeth, with the surrounding land being either
urban/suburban environments, or Newark Bay. The station itself has an aspirated thermistor to
record temperatures roughly 5 feet above a grassy surface. In addition to direct temperature
measurements, Heat Index (Steadman 1979) was calculated from the observed temperatures and
relative humidity. From this information, the number of days, number of hours, and time of day
where observed temperatures rise above 90° or 100°, as well as the duration and characteristics of
sustained heat waves was determined (see Results).
Epidemiology
Along with the determination of the extent and nature of extreme heat events in the
Newark area, it is necessary to assess the public health impacts of these events. Drs. Elena
Naumova and Ken Chui, at Tufts University, have been working on a time-series analysis of the
relationship between gastrointestinal infections in the U.S. elderly population and extreme heat
events, using 8.6 million hospitalization records from the Centers for Medicare and Medicaid
Services (CMS) databases in 122 cities in the continental United States. This problem is
methodologically challenging, as there is likely a time-lag between a peak in temperature and a
peak in observed infectious disease cases, as most gastrointestinal infections have a short but
tangible incubation period before symptoms appear and require the patient to seek medical care.
The analysis (based on established techniques; cf. Naumova et al. 2007, Naumova and
MacNeill 2005, and Lofgren et al. 2007), used a Poisson regression model with time-distributed
effects, which proved in simulation studies to ably predict case numbers in outbreaks, and had
54
superior performance to similarly construction Gaussian statistical models (see Results). The
regression equation is as follows:
\og[E{Y)} = pz+P\t+p2t2+P/+P^H2ncQt)+p,cos{2ncot) + l3b{Lit) + £ (Eq. 1)
Briefly, the log number of expected cases is predicted by an intercept (Bo), three terms for linear
and higher-order trends over time (B1.3), two terms for seasonal fluctuations in disease incidence
(B4.5) and finally a term for extreme heat events in the time series (136), as well as an error term. It
is B6 that is of special interest, as it describes the excess mortality due to extreme heat events not
otherwise explainable by long-term trends in disease incidence, or the seasonal disease patterns
many gastrointestinal infections exhibit. The difference in peak timing between gastrointestinal
disease and temperature, as well as the variability in these rates, was assessed. See Results for
examples of this analysis.
Operations Research
Once workable estimates of disease burden and the extent of extreme heat events in the
Newark area was determined, optimization models were used to determine optimal response
strategies to heat-emergency related evacuations, and compared to a model using more
"realistic" solutions one might typically see on the ground (see Agent-Based Simulations section
below), to compare how far from a theoretically optimal solution the more practical, realistic
scenarios deviate. These models relied on parameter estimates from the Epidemiology and
Climatology investigations and also from available published literature (Huang et al. 2010,
Doherty et al. 2009, El-Zein et al. 2004, El-Zein and Twetwel-Salem 2005, and Lin 2009)
Drs. Melike Baykal-Gursoy and Endre Boros headed the operations research component
of the project. Two models seeking purely optimal results were developed, one for the optimal
55
positioning of response centers, staffing and opening them from a list of known possible
locations, such as local high schools or hospitals - as well as the allocation of a finite amount of
resources, such as trained nurses, bottles of water, oxygen, EKG monitors, 02 SAT meters,
defibrillators, IVs bags and stands, and cots. The objective of this model was to limit the number
of people not served by facilities, resources or both, and thus at risk of death or illness due to
non-treatment. These models assumed that individuals began their travels from home (according
to US Census distribution) and experienced adverse heat-related health outcomes based on rates
extracted from the Epidemiological averages analyzed (described below), first in a case of lowlevel extreme heat (LLEH), and then for a case of high-level extreme heat (HLEH). Individuals
in each of these scenarios were limited spatially (rather than temporally) in their travel
capabilities, required to seek care within either a 1-mile or 2-mile radius. This model included
explicit facility capacity and resource availability (depleted by providing care in each additional
unit by individual need).
The second optimization model concerned the location of evacuation centers (as above),
but focused on the logistical aspects of evacuation of at-risk individuals from their homes to
those evacuation centers. The scope of that problem is two-fold - both how to assign particular
individuals to a location, and route them efficiently during an emergency. The area under study
for this model is a portion of the Newark area (Figure 2), using Schools, Libraries and Hospitals
(again see Figure 2) as potential evacuation center sites as a proof of concept for a more complex
model spanning a larger area. Based on hospitalization rates for the Newark area provided by Dr.
Naumova, hospitalization rates for three conditions, dehydration, cardiovascular disease and
respiratory disease were modeled as potentially fatal outcomes, and three scenarios were
modeled: the use of hospitals only as evacuation centers, hospitals and other cooling centers
56
(such as schools or libraries converted temporarily for that purpose), and hospitals, other cooling
centers and mobile acute care centers, all assuming a 7-day long heat event.
Agent-Based Simulation of Practical Routing Algorithms
Theoretically optimal solutions are only useful if they can be achieved in practice. We
therefore additionally built a series of simulation models, in which individuals would travel
particular routes (from home to a non-home location and back) around the city. The number of
individuals 'living' on each city block was determined according to US Census data (see Figure
11). Travel along routes was limited by realistic road capacity (based on OpenStreetMap data),
allowing for traffic jams to slow travel depending on demand over time. In cases of extreme heat,
individuals had an independent probability of developing each type of heat-related health
concern (e.g. dehydration, respiratory difficulty, or cardiovascular complications, or some
combination thereof). Each individual then had an independently assigned 'delay duration'
between onset of heat-related reaction and recognition that health care was needed. Once
recognition of care was achieved, individuals headed for a health-care provision facility.
Facilities were assumed to provide one of three different levels of care: Level 1 - Public
Schools Converted to Cooling Centers (assumed to have a capacity to care for 60 individuals at a
time, able to care for individuals experiencing dehydration), Level 2 - Public Libraries
Converted to Intermediate-Care Medical Facilities (assumed to have a capacity of 30 individuals
at a time, able to care for individuals experiencing dehydration or respiratory distress), and Level
3 - Hospitals (assumed to have an available capacity to dedicate to heat-related illness of 60
individuals at a time, able to care for individuals experiencing dehydration, respiratory distress,
or cardiovascular distress).
57
Individuals chose which facility to target based on one of three initial experimental
algorithms: Nearest, Nearest Appropriate, and Nearest Exact. In the 'Nearest' case, each
individual travels to the facility nearest to their current position, regardless of their need or the
capability of the facility. In the 'Nearest Appropriate' case, each individual is able to recognize
their own needs, and travel to the nearest facility that is able to provide treatment according to, or
in excess of, their needs (i.e. even someone experiencing only minor dehydration may travel to a
Hospital). Lastly, in the 'Nearest Appropriate Exact' case, each individual correctly identifies
their own healthcare needs and travels to the nearest center able to treat at most their specific
condition (i.e. dehydrated individuals will travel only to schools, individuals in respiratory
distress will travel only to libraries, and individuals in cardiovascular distress will travel only to
hospitals). Each of these scenarios require varying levels of cooperation and accuracy in selfdiagnosis, but were considered as an initial set of cases, even though many more scenarios
(expanding levels of knowledge, communication, and cooperation from the public) are
underway.
Upon arrival at a treatment center, individuals were admitted (regardless of
appropriateness of agreement in need to available care) so long as the facility had not yet reached
capacity. Once admitted, individuals remained in the care of the facility for the remainder of the
simulation. Individuals who arrived at a facility after the facility had reached its capacity were
turned away, unable to receive care from the facility.
These simulations all employed an 'Extreme-Scenario' in which individuals who were
unable to receive care at their first-choice facility were not redirected to other shelters, but
instead waited (futilely) by that shelter. Further, heat exposure was assumed to lead uniformly to
death if left untreated, even if the external, climatological level of heat itself fell (i.e. individuals
58
who began to experience any adverse reaction to heat exposure were required to receive care at a
health-care facility within a certain amount of time, or else were assumed to become worse,
eventually leading to death at a rate dependent on the type of adverse reaction experienced). This
Extreme-Scenario therefore implied that individuals whose first-choice shelter was full upon
their arrival were very likely to die. This was done not due to any belief in the realism of the
scenario, but to explore a 'worst case' outcome for the routing algorithm.
The input data for these simulations is listed in Table 3. Simulations were iterated under
Monte Carlo Simulation until the variance in the outcomes was seen to converge.
RESULTS
Climatology
The Newark area spends a not inconsiderable amount of time with temperatures above
90°. On average, there are 124 hours spent at or above 90° in a year, with a maximum in the 14year period of 319 hours (2010) and a minimum of 30 (2004). 90° or higher are reached on
average 26 days annually, with a maximum of 54 days (2010) and a minimum of 13 days (2004).
Using the Heat Index, a more physiologically grounded measure of how hot a person would
"feel" on those days, these figures rise considerably. 197 hours are spent above 90°, with a
maximum of 406 (2010) and a minimum of 85 (2009), and the heat index reaches or exceeds 90°
30 days annually, with a maximum of 58 days (2010) and a minimum of 16 days (2009). These
temperatures begin rising around noon, and typically peak in the early to late afternoon, with
temperatures not reaching morning levels until late at night. These results are summarized in
Figures 3-5.
Sustained temperatures over 100° are somewhat rarer. On average, 1.43 days per year
reach above 100°, with a maximum of 4 (2010) and a minimum of 0 (several years). Measured
59
by Heat Index, this number rises to 3.71 days per year, with a maximum of 8 (2002) and a
minimum of 0 (2000) (Figure 6). On average 2.4 hours are spent annually above 100°, with a
maximum of 12 (2010) and a minimum of 0 (several years). There is however, considerable
variability at this temperature level, especially when using the Heat Index (Figure 7).
In addition to the question of how often temperatures reach 'high temperatures' is the
nature of heat waves - sustained periods of at least three consecutive days of maximum
temperatures at or above 90°. On average, these periods last for 5 days, with the longest lasting a
full 14 days (July 16 to July 29, 2010). The average hourly duration, from the first hourly reading
over 90° to the last was 93 hours, with the maximum being 317 hours (same interval as above).
Only 26% of hours in these intervals are spent below 80°, with a minimum of only 4% of hours
spent below 80° (August 11th to August 14th, 2005). On average, four of these events occur per
year, with a maximum of 7 (1999, 2010) and a minimum of 0 (2004). Evidently, heat waves and
sustained periods of high temperature are not infrequent occurrences in the Newark area, and,
assuming there are appreciable health impacts from these events (see below), planning for heat
related emergencies is warranted.
Epidemiology
Gastrointestinal related hospitalizations are a significant public health burden in the U.S.
Elderly. During the study period (1991 to 2004), there were 8.6 million hospitalizations with
ICD (International Classification of Disease) codes related to gastrointestinal illness (Table 1).
Both gastrointestinal infections ("GI", ICD codes 001-009) and non-specific gastrointestinal
symptoms ("GS", ICD codes 558.9 and 787) peak in the spring months. GI is driven primarily by
ICD code 008 - Other organisms not elsewhere classified. The timing of these illnesses (Figure
6o
8) is consistent with viral infections, suggesting inadequate testing for them in elderly
populations - unsurprising, as these organisms are often difficult to culture.
Examining the magnitude of the time-distributed regression model's term for the effect of
extreme heat events (136 in the methods section), there is a significant effect of extreme heat
events on the incidence of gastrointestinal illness, with a time-lag corresponding with many
infectious diseases - and there is some evidence of a threshold effect depending on the
temperature cut-point selected for the definition of an extreme heat event (ranging from 90th
percentile of temperature to 96th percentile). This effect is present but less pronounced for nonspecific gastrointestinal illness. There is however variability in both the observed incubation
times and threshold effects. Examples of the analysis of two cities in the analysis, Boston and
Cleveland, are shown in Figure 9.
Operations Research
The resource optimization model was run on an initial allocation-of-resources-to-shelters
scenarios. This allocation scenario, run under both heat scenarios, and with both travel radii
capabilities, converged to a solution in at most 5200 seconds (a little under 1.5 hours), and
terminated entirely in at most 13334 seconds (a little under 4 hours), making them practical for
real-time decision making support, once the initial implementation has been achieved for a given
location/scenario, and the scenario-appropriate input data has been determined. Further, they
exhibited at worst a 0.1% rate in relative error. (For specific results, see Table 7, though these
were run more to investigate utility of these methods for real-time decision support.)
The evacuation routing model was successfully able to reduce the number of casualties
due to the simulated heat emergency, in some scenarios eliminating extra casualties due to the
event entirely (Table 2). Overall, even with sustained temperatures in the vicinity of 100°, the
6i
combined use of hospitals, civic structures converted to cooling centers and mobile acute care
centers was able to reduce the excess casualties from a heat related emergency by an order of
magnitude over hospital-only strategies, let alone strategies that do not involve evacuating at-risk
populations to stable shelters where they can find supplies and care. An example of block-level
evacuation routing patterns generated by the first model can be seen in Figure 10.
Agent-Based Simulation of Practical Routing Algorithms
These investigations into the impact of having to effectively communicate simple
strategies for evacuee routing to the public (rather than trying to communicate actually optimal
strategies, which may be incredibly intricate), under the Extreme-Scenario showed some very
promising initial results. No differences were seen in the number of individuals experiencing
adverse reactions to heat events among the three initially tested routing algorithms (Figure 12).
The expected number of deaths was artificially high due to the Extreme-Scenario assumption
(Figure 13), however still provided valuable insight by allowing identification of facilities and
traffic jams most likely to be responsible for increased numbers of fatalities by overcrowding
(Figures 17-19; Tables 4-6). The 'Nearest Appropriate' algorithm was seen to result in
substantial overcrowding at higher level facilities by lower-risk individuals, leading to
bottlenecks in service provision (Figure 14). This exemplifies the type of trade-offs that can be
expected between individual- and public-level motivation in health-care-seeking behavior. The
'Nearest' algorithm showed a distinct advantage over the other two algorithms when compared
under the metric of 'Percent of Facilities at or Exceeding Capacity', however this was likely due
to the Extreme-Scenario assumption of no redirection (Figure 15). Similarly, the results for the
number of locations affected by traffic jams likely reflected only the severity of the Extreme-
62
Scenario (Figure 16) and actual insight from this effort will be gained during later investigations
under different scenarios.
CONCLUSIONS AND RECOMMENDATIONS
Summary
Our studies show that shelter location, resource allocation to shelters, and the ability to
rapidly and accurately communicate appropriate evacuation routes to the public will drastically
affect the success of public health preparedness/intervention strategies for heat-events. We
further demonstrate the utility of coupling theoretical optimization strategies with simulationbased methods to determine likely outcomes for public health interventions.
Conclusions
We conclude that adopted evacuation routing and resource allocation strategies should
not attempt to attain theoretically optimal outcomes, but should rather choose the closest
practicable approximation.
Recommendations
We recommend that the locations pin-pointed by the evacuation routing model as traffic
jams and surge-overwhelmed facilities receive particular planning and attention (perhaps being
scheduled to receive supplementary resources or personnel) to alleviate congestion and minimize
negative outcomes. We anticipate further recommendations as the work to expand the agentbased simulations continues (under additional, external funding).
63
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Naumova, E.N., I.B. MacNeill, "Signature-forecasting and early outbreak detection system."
Environmeterics. Vol. 16 (2005)
Naumova, E.N., J. Jagai, B. Matyas, A. DeMaria et al., "Seasonably in six enterically transmitted
diseases and ambient temperature" Epidemiology and Infection. Vol. 135 (2007)
"OpenStreetMap". OpenStreetMap contributors, shared under a Creative Commons AttributeShare Alike license, http://www.openstreetmap.org (2010)
64
Steadman, R.G. "The assessment of sultriness. Part I: A temperature-humidity index based on
human physiology and clothing science." Journal ofApplied Meteorology, vol 18. (1979)
U.S. Census Bureau. "2010 Census Centers of Population by Census Block Group".
http://www.census.gov/geo/www/2010census/centerpop2010/blkgrp/bgcenters.html
(2011)
Whitman, S., G. Good, E.R. Donoghue, N. Benbow et al. "Mortality in Chicago Attributed to the
July 1995 Heat Wave". American Journal of Public Health, vol. 87, no. 5. (September
1997).
65
FIGURES
Figure 1: Location of Newark Liberty International Airport - indicated by Marker A - and surrounding areas (< 2011 Google)
66
Figure 2: Area of study for evacuation routing optimization model of the Newark area, with potential evacuation sites and
local zoning.
67
>90°F Days per Year
1997-2010
I Heat Index
I Temperature
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Figure 3: Days spent at or above 90° per year from 1997 to 2010. Figure includes both absolute measured temperatures as
reported by the Newark Airport ASOS, as well as calculated heat index.
>90 F Hourly Observations per Year
1997-2010
I Heat Index
I Temperature
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Figure 4: Hourly observations at or above 90° per year from 1997 to 2010. Figure includes both absolute measured
temperatures as reported by the Newark Airport ASOS, as well as calculated heat index.
68
Average Days Per Year 290°F By Hour
1997-2009
25
20
-tei
15
I Heat Index
l Temperature
10
—
I
_*:-
I
III••••
^ v^ ^ v2$\W^ ^ ^ ^ v^ rf* <J^ ri* ^ <?^ <?^ ^<?^ <J^ o* <^~ <^~ ^ v^ »^
Time
Figure 5: Average days per year spent at or above 90° per year by hour, from 1997 to 2009. Note 2010 measurements are not
included in this figure. Figure includes both absolute measured temperatures as reported by the Newark Airport ASOS, as
well as calculated heat index.
£100°F Days per Year
1997-2010
1
"
'
'•"
.•*-..
"
l Heat Index
I Temperature
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
Figure 6: Days spent at or above 100° per year from 1997 to 2010. Figure includes both absolute measured temperatures as
reported by the Newark Airport ASOS, as well as calculated heat index.
69
>100°F Hourly Observations by Year
1997-2010
60
~t~,—i—~'~
50
40
I Heat Index
I Temperature
3 30
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Figure 7: Hourly observations at or above 100° per year from 1997 to 2010. Figure includes both absolute measured
temperatures as reported by the Newark Airport ASOS, as well as calculated heat index.
70
n = 8.6 million
787: Symptoms of digestive system
558.9: Unspecified/noninfectious
008: Gl due to other organisms
009: Ill-defined intestinal infections
003: Salmonella infections
005: Other food poisoning
007: Protozoal intestinal diseases
004: Shigellosis
006: Amebiasis
001: Cholera
002: Typhoid & paratyphoid fevers
Figure 8: Distribution and timing of gastrointestinal hospitalization among U.S. elderly in 122 cities.
71
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Figure 9: Magnitude of the estimated effect of extreme heat events on gastrointestinal infections (Gl) and unspecified
gastrointestinal disease (GS).
72
Figure 10: Example modeled evacuation routing scheme to cooling and treatment centers in the Newark area.
73
Figure 11: Map displaying census blocks (with centers noted in red) in Newark, NJ.
74
% Sick overTime (by Routing Algorithm)
f
K-
K-
I
•
£ 50-
i
::
••;•
1
Groups
i
i
i
•
i
o
0 — Routing AljonttmsN
100
Ittrf
+ — Routing Algorithm*^
::-:
• — RoutirtJ Aijontnrn=N«
Figure 12: Extreme-Scenario percent of population suffering adverse reactions to heat exposure over
time in Practical Routing Algorithms.
As expected, the rates of increasing adverse reactions over time are virtually the same, owing simply to the assumed
linear increase in exposure and the fact that there is no recovery after the onset of adverse reactions to heat until the
individual receives treatment at a treatment center. They show neither benefit nor cost in percent of population
affected by heat due to choice of practical routing strategy.
75
% Dead over Time (by Routing Algorithm)
40-
J0*
1
i
•«
i
0
IX
lt«r»
Groups
O — Roulir>j Aljontttm^N
+ — RoutinjAt)onlrim=tJA
0—RoblinjAljorithin^NAE
Figure 13: Extreme-Scenario Percent of Population "Dead" over Time in Practical Routing
Algorithms.
The segmented behavior is expected, again due to the fact these simulations required treatment to recover from the
effects of heat. If treatment was not obtained within a fixed amount of time, the individual was assumed to have died.
This extreme scenario was explored to detect differences in efficiency in practical routing options.
The "Nearest" algorithm has a clear advantage over both other algorithms, since in this implementation all
individuals were accepted at all treatment centers, regardless of health concern or type of facility resources available
at the location. Therefore, individuals of all conditions were likely to be within a short distance of a school (the most
dense option) whenever they developed a need for care. In comparison, when the algorithm directed individuals to
only appropriate centers, according to their condition, those with more serious conditions were likely to require
greater travel time to arrive at a center that had the appropriate equipment for their care.
We further expect that expanding the simulations to include "rejection" (i.e. re-direction to the nearest careappropriate center) that the number of deaths seen when simulating the "Nearest" algorithm will rise significantly
and should be slightly larger than both other algorithms, due to the additional travel time required to travel to the
"incorrect" first-choice center.
76
% Being Treated over Time (by Routing Algorithm)
100"
90-
80-
73-
60-
I
S
50
.
40-
"
33-
20"
10-
0
Groups
0
Routing AlgortvTFN
100
lt*r#
+•—Routing A>gonlhfin*W
O — Routing A*gontim=Nft£
Figure 14: Extreme-Scenario Percent of Population Receiving Treatment over Time in Practical
Routing Algorithms.
The "Nearest" algorithm again shows a clear advantage over the other algorithms with respect to the number of
people who are in a treatment center over time. This again results from the significant probability of being in close
proximity to a school at any point in the travel network. The fact that the "Nearest Appropriate" algorithm does
slightly worse (and results in slightly more deaths) is likely due to overcrowding by low-risk individuals at the lowercapacity Level 2 and Level 3 centers which disallows higher-risk individuals from getting the necessary treatment at
these centers.
77
% of Ail Centers at Capacity (by Routing Algorithm)
13-
! I-
10"
3-
7-
6"
0
100
Hart
GruupS
• Routing AlgorthfTFM
+
—
Routing Algonthnr+*k
• •" Routing AtaontvrFM»£
Figure 15: Extreme-Scenario Percent of All Centers at or Exceeding Capacity.
The "Nearest" algorithm again shows an advantage over the other algorithms, however, this is likely due to needinappropriate care centers absorbing individuals who would otherwise overwhelm lower-capacity need-appropriate
locations. We again anticipate these results changing once the model is expanded to include 'rejection', however, the
insight provided by these extreme scenarios is a critical first step.
78
^•1
Number of Nodes Jammed over Time (by Routing Algorithm)
^^^^^^""
35"
3 3-
25-
20-
10-
B1
100
0
Groups
G "•• Routing Algorithm^
+ """Routing AlgontnrrFNA
I
zoo
O ^Routing AlgontnprFfiO£
Figure 16: Extreme-Scenario Number of Locations Blocked by Traffic Jams over Time in the Practical
Routing Algorithms.
The number of locations backed up by traffic were not seen to be significantly different across routing strategies until
after untreated individuals began dying, alleviating congestion along the roadways. This is a result of both the
extreme assumption of death in the model, and the lack of'rejection' causing redirection to different facilities. We
expect these results to change when both of these assumptions are altered, however, the insight provided by these
extreme scenarios is a critical first step.
79
Figure 17: Extreme-Scenario Locations Where the Practical Routing Algorithms Predict Clusters of
Deaths When Routing According to the "Nearest" Algorithm.
In the "Nearest" Algorithm, under the assumptions of the Extreme-Scenario, individuals were not redirected from
their initial target center, even if that center was unable to treat their condition or was already at capacity. Therefore
these individuals died while waiting for treatment. Centers where many of these deaths were predicted are indicated
in Red (bonfires). Other individuals were prevented from reaching shelters in time to receive care by traffic jams
clogging the roadways. These locations are indicated in Blue (snowflakes). (For further detail, see Table 3.)
80
Figure 18: Extreme-Scenario Locations Where the Practical Routing Algorithms Predict Clusters of
Deaths When Routing According to the "Nearest Appropriate" Algorithm.
In the "Nearest Appropriate" Algorithm, under the assumptions of the Extreme-Scenario, individuals were not
redirected from their initial target center, even if that center was already at capacity. Therefore these individuals died
while waiting for treatment. Centers where many of these deaths were predicted are indicated in Red (bonfires). In
contrast to the "Nearest" algorithm, no particular locations were found to increase mortality risks due to traffic jams
clogging the roadways. (For further detail, see Table 4.)
8l
Figure 19: Extreme-Scenario Locations Where the Practical Routing Algorithms Predict Clusters of
Deaths When Routing According to the "Nearest Appropriate Exact" Algorithm.
In the "Nearest Appropriate Exact" algorithm, under the assumptions of the Extreme-Scenario, individuals were not
redirected from their initial target center, even if that center was already at capacity. Therefore these individuals died
while waiting for treatment. Centers where many of these deaths were predicted are indicated in Red (bonfires). In
contrast to the "Nearest" algorithm, no particular locations were found to increase mortality risks due to traffic jams
clogging the roadways. (For further detail, see Table 5.)
82
TABLES
Table 1: Number of hospitalizations attributed to Gl related illness in the U.S. elderly aged 65 or
over between 1997 and 2004.
ICD-9 Code
Condition
#of
Hospitalizations
Cholera
Typhoid and paratyphoid fevers
Salmonellosis
Shigellosis
Other food poisoning (bacterial)
Amebiasis
Other protozoal intestinal diseases
Intestinal infection due to other organisms
Ill-defined intestinal infections
Symptoms involving digestive system
Other & unspecified non-infectious
gastroenteritis & colitis
Notes: ICD-9: International Classification of Diseases 9. Totals do not sum to 8,600,000 due to
multiple ICD-9 codes per hospitalization in some records.
001
002
003
004
005
006
007
008
009
787
558.9
Table 2: Number of excess casualties during a simulated heat-related emergency in the Newark
area, under increasing levels of evacuation shelter coverage.
Scenario:
Hospitals only
Hospitals and Cooling Centers
Hospitals, Cooling Centers, and Mobile Acute Care
Temperature
86o 90o 95o 100O
Excess Casualties
335 494 796 1307
224 301 416 555
0
55
0
0
Notes: 115 Cooling Centers and 8 Mobile Acute Care centers were used.
83
Table 3: Input Data for the Practical Routing Algorithms.
Input
Data Source
Map
Road network and geographic data from OpenStreetMaps, road
capacities set according to categorizations given by same
Used U.S. Census data to provide
• number of people per census block center
• geographic locations of census block centers
Geographic locations given in GIS shape files provided by Center for
Remote Sensing and Spatial Analysis at Rutgers University
(CRSSA)
Building capacities arbitrarily set according to facility "treatment level
type" (see Agent-Based Simulation section above)
Linear growth heat exposure arbitrarily parameterized around the
desired running time of the model
Population
Available Public
Facilities
Adverse HeatRelated Health
Outcome
Routing
Algorithm
Choice of (Nearest, Nearest Appropriate, Nearest Appropriate Exact);
simple self-explanatory routing methods which may be easily
communicated via public media for self-determination of
need/action by the public
84
Table 4: Extreme-Scenario Results from "Nearest" Algorithm in Practical Routing Algorithms.
Type
Center Waiting For
# Simulations
Appeared In
%of
Simulations
Appeared In
Total Deaths
over all
Simulations
Average
Deaths per
Simulation
Max Deaths
Per
Simulation
School
Raphael Hernandez School
187
93.5
484
2.588235294
6
Library
75 Alexander St
142
71
976
6.873239437
18
1941
14.27205882
44
School
Raphael Hernandez School
136
68
School
Hawthorne Avenue School
131
65.5
202
1.541984733
4
School
Branch Brook School
95
47.5
1967
20.70526316
50
Library
140VanBurenSt
75
37.5
343
4.573333333
14
Library
5 Washington St
74
37
295
3.986486487
15
School
Madison Avenue School
65
32.5
733
11.27692308
59
School
Raphael Hernandez School
65
32.5
82
1.261538462
3
Library
50 Hayes St
51
25.5
760
14.90196078
43
•
School
Dr. William H. Horton School
24.5
595
12.14285714
38
Library
34 Commerce St
44
22
159
3.613636364
9
None
Traffic Jam
23
11.5
24
1.043478261
2
School
McKinley School
22
11
2335
106.1363636
166
School
Belmont Runyon School
20
10
21
1.05
2
Table 5 : Extreme-Scenario Results from "Nearest Appropriate' ' Algorithm in Practical Routing
Algorith ms.
Type
Center Waiting For
# Simulations
Appeared In
%of
Simulations
Appeared In
Total Deaths
over all
Simulations
Average
Deaths per
Simulation
Max Deaths
Per
Simulation
Hospital
U.M.D.NJ.
200
100
43762
218.81
268
Library
50 Hayes St
200
100
29505
147.525
20S
Library
739 Bergen St
200
100
27528
137.64
187
Hospital
Newark Beth Israel Medical Center
200
100
21164
105.82
145
Library
75 Alexander St
200
100
18386
91.93
127
School
Burnet Street School
200
100
15502
77.51
178
Library
235 Clifton Avenue
200
100
15205
76.025
115
Hospital
St. James Hospital
200
100
14596
72.98
99
Library
99 Fifth St
200
100
10049
50.245
83
Library
355 OsborneTerr
200
100
9761
48.805
81
Library
140 Van Buren St
200
100
8136
40.68
66
Hospital
Columbus Hospital
200
100
7442
37.21
65
Library
5 Washington St
200
100
5141
25.705
50
Library
34 Commerce St
200
100
4845
24.225
43
100
3542
17.71
36
Library
722 Summer Ave
200
85
Table 6: Extreme-Scenario Results from "Nearest Appropriate Exact" Algorithm in Practical
Routing Algorithms.
Type
Center Waiting For
# Simulations
Appeared In
%of
Simulations
Appeared In
Total Deaths
over all
Simulations
Average
Deaths per
Simulation
Max Deaths
Per
Simulation
200
100
39046
195.23
255
130.94
163
Library
50 Hayes St
Hospital
U.M.D.NJ.
200
100
26188
Library
739 Bergen St
200
100
25736
128.68
164
Library
235 Clifton Ave
200
100
20555
102.775
137
Library
355 Osborne Terr
200
100
18540
92.7
118
Library
99 Fifth St
200
100
16765
83.825
136
Library
75 Alexander St
200
100
16116
80.58
107
Library
140 Van Buren St
200
100
12927
64.635
84
Hospital
Newark Beth Israel Medical Center
200
100
9745
48.725
75
Library
34 Commerce St
200
100
8665
43.325
62
Library
5 Washington St
200
100
7961
39.805
71
Library
722 Summer Ave
200
100
3700
18.5
36
Hospital
St. Michael's Medical Center
199
99.5
4867
24.45728643
61
School
Raphael Hernandez School
177
88.5
365
2.062146893
5
Hospital
St James Hospital
174
87
1871
10.75287356
32
Table 7: Results from Example Resource-Allocation-to-Shelters Scenarios from the OR
optimization model.
1 mile Travel Radius
Total ft
Total ft Deaths
Adversely
Expected
(Worst Case)
Effected
People
LLEH
HLEH
659
659
30(140)
30(140)
2 mile Travel Radius
Total ft
Adversely
Effected
People
659
659
Total ft Deaths
Expected
(Worst Case)
30(140)
6(66)
REFERENCE DOCUMENTATION:
Figure 1: Copyright owner is Google, Inc. Their permissions page may be found at
http://www.google.com/permissions/geoguidelines.html
86
CHAPTER 4
Project 09 05 F: Supply Chain of Critical Medical Resources for Emergency Situations
87
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Supply Chain of Critical Medical Resources for Emergency Situations
Project 09 05 F
Final Report
Investigator:
Dr. Tayfur Altiok, SOE, ISE, CAIT, Rutgers University
Email: Altiok@rci.rutgers.edu
Research Associates: Dr. Wei Xiong, Weill Medical College, Cornell University
Mustafa Rawat, MSIE, Rutgers University
Selim Bora, PhD student, RUTCOR, Rutgers University
88
UCDPER Acknowledgement and Disclaimer Statements
This project was sponsored by the University Center for Disaster Preparedness and
Emergency Response (UCDPER) - A Collaborative Initiative of Rutgers, The State University of
New Jersey, UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson
University Hospital- with support from Department of Defense Grant No. W9132T-10-1-0001.
We would like to give special thanks to Nathaniel Hupert, Associate Professor of Public
Health and Medicine in Department of Public Health, Weill Cornell Medical College and Senior
Medical Advisor of Preparedness Modeling Unit at U.S. Centers for Disease Control and
Prevention (CDC), for his guidance and support in building our Virtual Hospital Module. We
also thank for his help in validating parameters used in the epidemiological model.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
89
Abstract
The research carried out in this project addresses the issue of managing critical medical
supply inventories in hospital under surge scenarios. It is based on an approach that optimizes
inventory control parameters in hospital settings under scenarios such as the pandemic flu with
surging demand for medical supplies. We combined epidemiologic modeling techniques with
simulation and optimization modeling to provide the best strategy for inventory management
under surging demand in pandemic-like scenarios.
The project aims to provide a guideline to implement a formal procedure to effectively
control inventories of critical medical supplies and minimize inventory management costs while
maintaining an acceptable customer service level in pandemic-like scenarios.
Project involves a high-fidelity Disease Progress Module (DPM) Influenza Pandemic like
scenarios using already validated data from the historical epidemiological literature.
For the proposed simulation framework, a Virtual Hospital Module (VHM) was
developed to capture resource consumption in healthcare settings during an Influenza Pandemic.
A Dynamic Programming optimization model was constructed to optimally manage the
inventory of critical medical supplies (that is the decisions regarding when to order and how
much to order) in hospital settings. A number of numerical scenarios were analyzed and results
were obtained.
90
Foreword
This project was managed under the under the leadership of the University Center for
Disaster Preparedness and Emergency Response that is a collaborative initiative among the
UMDNJ - Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey
and the Robert Wood Johnson University Hospital. In particular, the project was carried out at
Rutgers University's Center for Advanced Infrastructure and Transportation.
91
TABLE OF CONTENTS
List of Figures
93
List of Tables
94
INTRODUCTION
95
Background
95
Objective
96
Approach
96
Scope
97
Mode of Technology Transfer
97
APPROACH
98
Epidemiological Model
98
The Virtual Hospital Module
101
Optimizing Inventory Management in the Virtual Hospital
105
User Interface
109
Case Studies
109
CONCLUSIONS AND RECOMMENDATIONS
112
REFERENCES
114
FIGURES
116
TABLES
124
92
LIST OF FIGURES
Figure 1. Schematic relationship between the five subgroups in the model
116
Figure 2. Treatment dynamics at the hospital
116
Figure 3. Length of stay in the virtual hospital
116
Figure 4. High level patient workflow
117
Figure 5. Daily product consumption by patients
117
Figure 6. Daily demand for Tamiflu
118
Figure 7. Inventory control model used in the hospital model
118
Figure 8. Disease Variables dialog box of the user interface
119
Figure 9. Hospital Model dialog box of the user interface
119
Figure 10. Cost Parameters dialog box of the user interface
120
Figure 11. Daily demand, total cost per day and the optimized target
inventory levels for products 1 in Case 1
120
Figure 12. Daily demand, total cost per day and the optimized target
inventory levels for products 2 in Case 1
121
Figure 13. Daily demand, total cost per day and the optimized target
inventory levels for products 3 in Case 1
121
Figure 14. Daily demand, total cost per day and the optimized target
inventory levels for products 1 in Case 2
122
Figure 15. Daily demand, total cost per day and the optimized target
inventory levels for products 2 in Case 2
122
Figure 16. Daily demand, total cost per day and the optimized target
inventory levels for products 3 in Case 2
123
Figure 17. Daily demand, total cost per day and the optimized target
inventory levels for products 1 in Case 3
123
93
LIST OF TABLES
Table 1. Epidemiological model parameters
124
94
INTRODUCTION
Background
In the decade before the 2009-10 influenza pandemic caused by the novel H1N1 virus
(pHlNl), the spread of the much more lethal H5N1 "avian" influenza in Asia and parts of Africa
raised concerns about the potentially devastating impact of a severe global influenza outbreak
(Salomon and Webster 2009) and (Chan 2009). In response, most developed countries and many
private corporations made considerable investments over the last decade in the purchase of
antiviral medications (AVMs) to treat those infected with influenza during a pandemic. To date,
most of the literature has addressed either pandemic mitigation or preservation of healthcare
workforce capacity during the peak of an outbreak. However, to the best of our knowledge, there
is no evidence-based research available in the medical literature that can guide healthcare
facilities to establish sufficient medical supply in order to maintain adequate surge capacity for
flu patients. In the inventory management and supply chain literature, most existing models
make the assumption of independence and time-homogeneity of the demand for medical supply.
However, during a pandemic, demand is uncertain and definitely non-stationary due to surge
dynamics. It grows exponentially in the early part of the pandemic and will decline when there
are less people that can be infected by the disease (herd immunity). The non-stationary demand
pattern raises a unique problem for inventory management since it becomes difficult to decide on
when to order and how much to order of each of the critical medical supplies. This is also a
significant research issue in the inventory management literature.
Non-stationarity of the
demand gives rise to complexities that make the problem mathematically and practically
intractable.
95
The research carried out in this project addresses the issue of inventory management that
basically deals with how much of each unit to maintain on hand using and approach that
optimizes inventory control system parameters in hospital settings under scenarios such as the
pandemic flu with surging demand for medical supplies. This is a critical issue since too much
of medical supply inventory can cost a lot while short inventories will not satisfy the demand.
Finding the right amount of stock is always a challenge in inventory management due to
uncertainties involved in demand.
The contribution of this research is in providing the proof of concept to hospital
management that tools such as the one developed in this research will be instrumental in
managing inventories once deployed in full scale.
Objective
The project aims to provide the proof of concept for developing an approach to handle
inventory management under surge scenarios in hospitals. The objective is to help hospitals in
making decisions regarding maintaining stocks of medical supplies.
This is achieved by
developing a formal procedure to help effectively control inventories of critical medical supplies
and minimize inventory management costs while maintaining an acceptable customer service
level in pandemic-like scenarios.
Approach
In essence, using a system of ordinary differential equations, a compartmental SEIR
model (May and Anderson 1991) is developed describing the transmission dynamics of a
pandemic in a large population. Then, the SEIR model is incorporated into a virtual hospital
simulator using ARENA simulation tool to identify daily random demand over the course of the
pandemic. Finally a dynamic programming algorithm is used to optimize the target inventory
96
levels as well as reorder points for the duration of the pandemic period. Clearly, the values of
these parameters vary over time due varying demand over the pandemic duration.
Scope
The project team received guidance from the Robert Wood Johnson University Hospital
on what these critical medical supplies may be as well as what kind of an inventory management
approach they may be using. Without lack of generality, the current approach employed in the
project includes three types of medical supplies and it can be easily extended to any number
supplies critical for the scenario at hand.
Mode of Technology Transfer
The methodology developed in the project is ready to be incorporated in a software tool
for a large-scale implementation for operational purposes. The tool would have a proper user
interface for parameter value entry for each of the supply items the hospital management wants
to include. Thus, the technology transfer can be achieved through software development for
decision making in inventory management in hospital setting.
97
APPROACH
In this research, epidemiological modeling techniques are combined with simulation and
optimization methodologies to provide the best strategy for managing inventories of critical
medical supplies in hospitals under surging demand scenarios in pandemic situations. On the
epidemiological front, we have used a SEIR compartmental model to generate a hypothetical
pandemic. This model produces a non-stationary patient flow into a virtual hospital where they
get appropriate treatment. During the period of the pandemic, a single-item inventory model,
assuming a non stationary demand process and a deterministic replenishment lead-time, is
implemented at the virtual hospital and used determine the safety stock requirements for the
item. This procedure is repeated for all three items considered on the study.
Below, each of the three components of the approach is described in detail.
Epidemiological Model
Mathematical models of the transmission of infectious agents are used to understand the
behaviors of an emerging pandemic influenza and to evaluate the effectiveness of various
intervention strategies. Using a system of ordinary differential equations (ODEs), we developed
a compartmental model describing the transmission dynamics of a pandemic in a large
population. A deterministic modeling framework was appropriate since our intention was to
capture disease-spread dynamics in a population in which a pandemic has already been initiated;
that is, we are not primarily concerned here with the effect of random perturbation of epidemic
ignition or quenching.
Individuals in a population are divided into standard modeling compartments such as
susceptible (S), exposed (E), infectious (I), recovered (R), and disease-induced dead (D). As
shown in Figure 1, susceptible individuals may become infected, incubate the infection, progress
98
to become fully contagious, and finally either recover and develop immunity from the disease or
die.
The arrows that connect the boxed subgroups represent movement of individuals.
Susceptible (S) will first become exposed (E). Exposed (E) will become infectious (I) after an
incubation period. Infectious (I) can either recover (R) after a recovery period or die (D).
Following the schematic representation in Figure 1, the disease spread and control
dynamics can be described by the following differential equations for a population of size N
(May and Anderson 1991).
S = -0IS/N
E = /3IS/N-KE,
i = KE-{y + S)I,
(1)
R = yl,
D = SI
Baseline values of the epidemiological model parameters used in the above model are
summarized in Table 1. Values of these parameters are based on published epidemiological,
clinical, and experimental data. The basic reproduction number R0, defined as the average
number of secondary cases generated from an average primary case in an entirely susceptible
population, is used to quantify the transmissibility of an infectious disease (Keeling and Rohani
2008). We first calibrated the model for a range of R0 values in order to reproduce typical
patterns of a pandemic influenza assuming a baseline of no interventions. The type of SEIR
model we have used, with simplified structures and assumptions, normally leads to an extremely
high attack rate with even moderate R0 values (McCaw and McVernon 2007). To simulate a
99
realistic epidemic outbreak, we have assume that R0 equals to 1.8, and 80% of the population in
our baseline case are susceptible at the start of the pandemic (Mills, Robins et al. 2004). This
assumption was motivated by the "herald wave" phenomenon observed in the 1918 pandemic
where those who recovered from influenza infection in the spring were protected from the
disease in the autumn pandemic because they acquired partial immunity from re-infection with
similar strains of the virus (Ferguson, Mallett et al. 2003).
Estimates of the duration of the asymptomatic phase of both seasonal and H1N1
pandemic influenza range from 1 to 2 days; we have further defined the symptomatic and
infectious phase to last 4 days (Longini, Halloran et al. 2004; Ferguson, Cummings et al. 2005;
McCaw and McVernon 2007).
(Khazeni, Bravata et al. 2009). The base case with no
intervention (neither prophylaxis nor vaccination) produces a unimodal epidemic with a
cumulative attack rate (CAR) of 35.9% lasting approximately 17 weeks from index cases to first
day with <0.1% increase in CAR.
This result is consistent with published pandemic
preparedness assumptions from the U.S. Centers for Disease Control and Prevention (CDC
2008).
We implemented the SEIR model for demand generation for medical supplies in our
virtual hospital simulator using the Arena simulation tool . ARENA uses the fourth-order RungeKutta method to solve the model numerically. The model predicts the number of infected
individuals throughout the pandemic and determines the potential impact and effectiveness of
intervention strategies and time-course of the epidemic in a region of one million people.
Limitations:
ARENA is a trademark of Rockwell Software.
100
We conducted a modeling study based on a number of assumptions regarding the disease
characteristics. Although these parameters are based on reported data in the literature, preparing
for influenza pandemic requires considering enormous uncertainty, such as the initiation of a
pandemic, the speed of spread, its level of virulence, and the extent of its resistance. In the
course of the pandemic, rapid decisions are required to be made in response to the pandemic as
more information about the epidemiologic profile becomes available. We have ignored this
process in our modeling effort due to its massive complexity.
Also, we did not consider the role of spatial and population heterogeneity in the spread of
influenza pandemic. Our deterministic modeling approach assumes homogenous population mix
where all individuals have the same contact and infection rates. A more realistic model should
include age, social contact, and spatial structure of the population under consideration.
In
particular, the contact network structure is critical in determining the spread of the pandemic in
the initial stage of the outbreak. Once an epidemic has begun, a deterministic model would
probably provide a reasonable description of the disease dynamics.
Although these limitations may suggest the need for a more involved modeling effort,
they are certainly outside of the context of this project. Our work highlights the importance of
operational considerations in pandemic preparedness planning. We have emphasized the use of a
modeling tool to assist policy makers in testing and providing insights on the effectiveness of
their decisions regarding inventory management of medical supplies taking into account the
uncertainties in a potential pandemic.
The Virtual Hospital Module
The virtual hospital module simulates the arrival and treatment of infected patients at
their local healthcare facility during an influenza pandemic. The top level model comprises of
101
two sub models: the first model calculates the ratio of patients that are susceptible, exposed,
infected, and recovered or dead in a given population size at any given point in time. The
number of patients in any of the above compartments is controlled by the disease progression
parameters defined in the epidemiology model or the Disease Progression Module (DPM). The
second model handles the daily consumption of medical supplies required for administering the
treatment of the general population during the influenza pandemic. Obviously, this is dependent
on the number of patients seeking treatment and their expected length of stay based on their
condition at the time of admittance.
From the differential equations set (1), we have the number of people infected I\t) by
the flu at any given time / given by
I\t) = K-E{t).
(2)
And therefore the number of new people P(t) that are added to the infectious compartment in a
day is shown below
I\t)-I\t-\) = P{t).
(3)
Each person seeking treatment can be categorized as type severe or moderate and their number at
any point in time / are Ps{t) or moderate Pm(t) and are given by
Ps(0 = P(t)-r-rl
Pm(t) = P(t)-r-(l-ri)
102
(4)
where r is the percentage of the population that seeks treatment, i.e. patients, and r{ is the
percentage of patients that are categorized as type severe.
Finally, the probability of a patient in severe condition dying, that is Pd , is shown below
prf =££>.«.,.(,;+c-(l->i))
(5)
where
D(t) = the number of patients deceased at any given point in time /
a = the attack rate
c = the percentage of moderate patients whose condition is expected to worsen and are therefore
categorized as severe patients and moved to the ICU from the hospital floor.
The Simulation Model
Equations (2) - (5) are incorporated into the Arena model to create the pandemic dynamic
and consequently the Arena model simulates the arrival and treatment of patients at the hospital.
Each entity in the simulation model represents a patient. The number of people arriving at the
hospital in any given day is determined by the epidemiological model. To model randomness in
the arrivals, a normal distribution with parameters (0, dv) is introduced where dv is the daily
variation. The variation factor is currently set at 20% of the number of people that are infected
by the influenza pandemic in any given day. We will be assuming only 10% of the affected
population seeking treatment at the hospital (Hupert 2010). This subset of the population is now
classified as patients for modeling purposes. We will also assume that 15% of the patients are
categorized as type severe and the rest as moderate (Hupert 2010). Patients whose condition is
severe seek treatment in the ICU, moderate condition patients on the other hand are routed to the
floor for treatment. Severe condition patients after completing their length of stay in the ICU are
103
moved to the floor. All discharges from the hospital always take place from the floor. The
treatment dynamics are pictorially represented in Figure 2 (AHRQ 2011).
Since the probability of a patient dying is already known, the model pre-designates
patients that are going to be deceased as they arrive at the hospital. Only patients whose
condition is severe die, furthermore their day of death coincides with their last day in the ICU.
These assumptions have been made to reduce the complexity of the model. Patients whose
condition is moderate are routed directly to the floor for treatment. We have assumed that the
condition of 5% of the incoming moderate patients will deteriorate and they will have to be
routed to the ICU for treatment. These patients will be reclassified as type severe and they may
potentially die. Once their condition improves in the ICU, they will be moved back to the floor
and then eventually discharged. The duration of their stay in the ICU and floor can vary by
patient and is generated using a triangular distribution with parameters shown in Figure 3. A
detailed patient process flow is shown in Figure 4.
Each entity representing a patient is routed appropriately to the ICU or the floor in the
workflow of the model.
Each entity is delayed in the model for its designated length of stay.
The rules regarding the movement of patients from the ICU to the floor or vice versa described
earlier are all enforced in the model. This obviously impacts the total number of patients in the
ICU and the floor on a daily basis. The model keeps tracks of the total quantity of products
consumed by all ICU and floor patients on a daily basis.
In this study three products are
considered. The quantity consumed per patient per day shown in Figure 5 and is dependent on
the condition of the patient, i.e. severe or moderate.
The Model's Output
104
The length of each replication is determined by the arrival of new patients at the hospital.
A simulation run terminates when new patients stop arriving and all existing patients are
discharged. In this study we run the model for 100 replications, the total consumption per
product per day is recorded into a Microsoft Excel spreadsheet. After completion of the final
replication, the mean and standard deviation of product daily consumption figures are obtained
and recorded in the same Excel file. For instance, Figure 6 shows the demand profile for one of
the products. From the demand profile we can see that the period of greatest activity during the
influenza pandemic is from days 16 to 76, or 60 days. The demand peaks somewhere around the
mid to early 40th day mark. This 60-day period is when the greatest strain on the healthcare
system is experienced.
Due to the inherent difficulty in forecasting the demand during an influenza pandemic,
hospitals will either tend to overstock or fall short on medical supplies. Both these outcomes can
have drastic consequences. Overstocking will lead to higher inventory management costs and
potential wastage due to disposal of unused limited shelf life medical products, leading to
increased operating costs for the hospital.
On the other hand, maintaining an inadequate
stockpile of critical products will impair its ability to treat patients, affect its customer service
level and potentially damage its reputation in the long term. Below, in the third part of this
study, we present an optimization model, incorporated into the virtual hospital model, to
determine the best possible target inventory levels based on inventory costs of the products
introduced earlier. Again, this is to provide a guideline to implement an inventory control policy
that minimizes inventory management costs while maintaining an acceptable customer service
level during such a surge period.
Optimizing Inventory Management in the Virtual Hospital
105
In this section, an optimization model and in particular a dynamic programming model is
introduced to obtain the optimum target inventories of the three products mentioned earlier in the
virtual hospital model. The optimization will be cost minimization based on inventory holding
cost, shortage cost and the cost of changing the target inventory level. The model is to be
incorporated into the simulation model to manage the inventories of the virtual hospital
introduced earlier.
The inventory management concept used here is presented in Figure 7.
We have
employed a periodic review model where the inventory of each product is monitored daily and a
replenishment order is placed every time the inventory is observed below its target level. The
order is place at 13:00 in the afternoon and is assumed to arrive by the end of the day,
presumably before the beginning of the next day. Below, we present the optimization model.
We will concentrate on a single product for analysis purposes. It is typical in the health
care industry that the lead time for an order arrival is roughly a day. Thus, we assume that the
lead time for an order, denoted by L, is at most a day, D, is demand for the product on day t, Q,
is the amount ordered on day t, and x, is the inventory on hand of the product at the beginning of
day?.
Note that the beginning inventory level on day t+1 can be expressed as
x,+1=(x,-D,)++a
where the order quantity is Qt = x -x,, and x is the target inventory level for the medical
supply in consideration.
106
(6)
On the other hand, by midday on day t+1, we also have
Vu =(*u-(A.i+A.2))++Q
(?)
where Q,=x- xl2, and the midday on hand inventory is x, 2 = (x,, - £),_,)+.
Assuming that the demand in the first half of the day will always be less than the day's starting
inventory, i.e. xn> Dn, we have
•"•(+1,1
-
x-Dl2,
x-xl2,
xl2>Dl2]
otherwise]
(Arrow, Karlin et. al 1958) shows that if current stock size x is a random variable with density
f(x), distribution associated with the random variable measuring stock available at the next
period,
Based on this construction, they have developed the following stationary distribution of the
beginning inventory level
4>(y)[l-W-y)]
l-[l-*oo][l-«(*-.>0]
where y the random variable representing the beginning inventory level and <t>( v) is the integral
of daily demand density function.
The probability that demand exceeds supply on a given day is given by
107
Pr{£>^}= \lf(yM€)d€dy=[<p(t)[F(t)]dt+[<p($)dt.
(9)
t>y
Accordingly, the expected quantity of unsatisfied demand is given by
E{penalty)= \\{S-y)f{y)q*£)cU;dy
(10)
= I cp(Z)dZ I (4 - y)f(y)dy + £ <p(£)rf# £ (| - y)f(y)dy
Also, the amount on hand at the end of a day is given by
E{handling) = [ [ (y - 4)<p^)f(y)d^dy.
(11)
Now we have the expected on-hand inventory level and the expected shortage level, we are ready
to evaluate the cost minimizing objective function for feasible levels of target inventory of the
product. The following algorithm will be used to generate the optimal target inventory level of
the medical supply we have under consideration.
The Algorithm for the Optimal Target Levels
Step 1:
Compute x, for every day / minimizing the expected total cost per day, that is
E [Quantity short] • p + E [On hand stock] • h .
Step 2:
Use dynamic programming to determine when the target inventory level must be
changed.
The dynamic programming formulation is done in such a way that the inventory level is the state,
and the days are the stages in the model. So, the possible states (target inventory values) are
x,,x2,---,x, . Also, let us define rc,(/,)to be the total cost up to day t, where the current
108
inventory level is I, -xx,x2,---,xT. Then, the following dynamic programming recursion can be
established.
rC/(/() = min-{P(x,) + rC,+,(/,+1)}
where
p(x,) = (i,-Dly *h+(D, -i,y*p+cc(x,)
and where
[0, otherwise)
Thus, I, = xvx2,---,xT gives us the optimal target inventory values for the medical supply under
consideration for days 1 through T of the surge period.
This algorithm can easily be
programmed in an operational environment and implemented for each product on a daily basis.
Below we provide a 3-product case scenario.
User Interface
We have built in the epidemiological model, the virtual hospital and the optimization
model into the simulation. It has a user interface to provide starting information for the model.
Screenshots from the user interface are shown in Figure 8-10. Disease Model dialog box
receives values for the parameters for the epidemiological model. Hospital Model dialog box
receives values for the parameters for virtual hospital. Cost Model dialog box receives values for
the parameters for the optimization model. Below we present cases studies.
Case Studies
109
In this section, we present three case studies of the virtual hospital with 3 products and varying
inventory costs. In all of these cases, we assume the following epidemiological parameter
settings:
Incubation Period:
Infectious Period:
Parameter 3:
N:
Case 1:
1.9 days
4.1 days
0.00063
100,000
Unit inventory holing cost: $8/day
Unit shortage cost:
$16/short
Cost of changing target level: $1,000
The daily demand, and the optimized target inventory levels for products 1 to 3 and the total cost
per day are shown in Figure 11 - 13. In this case, we have kept the values of th ecost parameters
at relatively low levels. This encoures frequent changes in target levels to adapt the chaing
environment due to surge.
Case 2:
Unit inventory holing cost: $8/day
Unit shortage cost:
$ 16/short
Cost of changing target level: $6,000/change
In this case, the cost of changing target inventory levels is increased by six fold. The daily
demand, and the optimized target inventory levels for products 1 to 3 and the total cost per day
are shown in Figure 14 - 16. Observe the impact of large target changing costs in the form of
much lesser changes of target values. Compare the blue lines in Figures 14 - 16 to Figures 11 13. Unit shortage cost is relatively higher than the unit holding cost and therefore the
optimization model tries to keep the target levels close to the demand and yet changes them
much less infrequently as compared to Case 1.
110
Case 3:
In this case, we will look into the impact of high unit holding costs. The cost parameters are
given below:
Unit inventory holing cost: $ 16/day
Unit shortage cost:
$8/short
Cost of changing target level: $6,000/change
Observe the impact of large unti holding cost which discourage holding high inventories and
push the target levels to low values as observed in Figure 17, shown only for product 1.
ill
CONCLUSIONS AND RECOMMENDATIONS
The research in this project addressed the issue of managing inventories of medical
supplies and especially the critical ones in hospitals under surge (pandemic) scenarios. Inventory
management basically deals with how much of each unit to maintain on hand by deciding on
when to order and how many to order for each item under consideration. This is a critical issue
since too much of medical supply inventory can cost a lot while short inventories will not satisfy
the demand. Finding the right amount of stock is always a challenge in inventory management
due to uncertainties involved in demand. We have developed a sound and practical approach
that combines epidemiologic modeling techniques with simulation and optimization modeling to
provide the best strategy for managing inventories. It involves a high-fidelity Disease Progress
Module Influenza Pandemic like scenarios using already validated data from the historical
epidemiological literature. For the proposed simulation framework, a Virtual Hospital Module
was developed to capture resource consumption in healthcare settings during an Influenza
Pandemic. A Dynamic Programming optimization model was constructed to optimally manage
the inventory of critical medical supplies (that is the decisions regarding when to order and how
much to order) in hospital settings. A number of numerical scenarios were analyzed and results
were obtained. The approach is quite practical and readily implementable in hospital settings.
Recommendations
As mentioned earlier, the project aims to provide guidelines to implement a formal
procedure to help decision makers managing inventories in the health care industry. Due to that
fact that shortcomings in medical supplies may end up in dire consequences, inventories are
typically held at higher levels as compared to other industries. Clearly, this has consequences
that appear as significant contributions to health care costs. A serious effort to reduce health care
112
related costs is to implement effective inventory management policies so that supply stocks are
kept at reasonable levels during times when there is no mass demand.
During emergency
scenarios such as pandemic situations the system should be effective enough to respond quickly
to build right amount of inventories.
Our recommendation to health care system managers is to focus on effective supply
management systems that are intelligent enough to respond high-consequence situations by rapid
inventory build ups and yet maintain lower yet sufficient levels of inventories at other times.
This will have reducing effects on health care costs in the U.S.
113
RFERENCES
AHRQ (Agency for Healthcare Research and Quality), Hospital Surge Model,
http://www.hospitalsurgemodel.org/, last accessed on July 28, 2011 (2011)
Arrow, K.J., S. Karlin, and H. E. Scarf, Studies in the Mathematical Theory of Inventory and
Production, Chapter 10, Stanford University Press (1958)
Butcher, J. C, Numerical Methods for Ordinary Differential Equations, John Wiley & Sons
(2008).
Chan, M. "World Now at the Start of 2009 Influenza Pandemic, Statement to the Press by WHO
Director-General." Volume, DOI (2009).
Ferguson, N. M., D. A. Cummings, et al., "Strategies for Containing an Emerging Influenza
Pandemic in Southeast Asia." Nature 437(7056): 209-14. (2005).
Ferguson, N. M., S. Mallett, et al., "A Population-Dynamic Model for Evaluating the Potential
Spread of Drug-Resistant Influenza Virus Infections During Community-Based Use of
Antivirals." J Antimicrob Chemother 51(4): 977-90 (2003).
Handel, A., I. M. Longini, Jr., et al., "Neuraminidase Inhibitor Resistance in Influenza: Assessing
the Danger of Its Generation and Spread." PLoS Comput Biol 3(12): e240 (2007).
HHS: Guidance on Antiviral Drug Use During an Influenza Pandemic (2008).
Hupert, N., Weill Medical College, Cornell University, Personal conversation (2010).
Keeling, M. J. and P. Rohani, Modeling Infectious Diseases in Humans and Animals, Princeton
University Press (2008).
Khazeni, N., D. M. Bravata, et al., "Systematic Review: Safety and Efficacy of ExtendedDuration Antiviral Chemoprophylaxis Against Pandemic and Seasonal Influenza." Ann
Intern Med 151(7): 464-73 (2009).
Longini, I. M., Jr., M. E. Halloran, et al., "Containing Pandemic Influenza With Antiviral
Agents." Am J Epidemiol 159(7): 623-633 (2004).
Marsh-Inc. and T.-A. Group-LLC: Corporate Pandemic Preparedness: Current Challenges to
and Best Practices for Building a More Resilient Enterprise (2007).
114
May, Robert Lewis, R. M., Anderson, Infectious Diseases of Humans: Dynamics and Control.
Oxford [Oxfordshire]: Oxford University Press. ISBN 0-19-854040-X (1991).
McCaw, J. M. and J. McVernon, "Prophylaxis or Treatment? Optimal Use of An Antiviral
Stockpile During An Influenza Pandemic." Math Biosci 209(2): 336-60 (2007).
Mills, C. E., J. M. Robins, et al., "Transmissibility of 1918 Pandemic Influenza." Nature
432(7019): 904-6 (2004).
Ong, C. W., K. Y. Ho, et al., "Reacting to The Emergence of Swine-Origin Influenza A H1N1."
Lancet Infect Pis 9(7): 397-8 (2009).
Rice, J. B. and F. Caniato, "Building a Secure and Resilient Supply Chain." Supply Chain
Management Review 7(5): 22-30 (2003).
Salomon, R. and R. G. Webster, "The Influenza Virus Enigma." CeU 136(3): 402-10 (2009).
Staples, J., "A New Type of Threat." Harvard Business Review 84(5): 20-22 (2006).
Ward, P., I. Small, et al., "Oseltamivir (Tamiflu) and Its Potential for Use in The Event of An
Influenza Pandemic." J Antimicrob Chemother 55 Suppl 1: i5-i21 (2005).
WHO: Pandemic (H1N1) 2009 Briefing Note 3 (2009).
115
FIGURES
Figure l. Schematic relationship between the five subgroups in the model
Figure 2. Treatment dynamics at the hospital
Days in the ICU
TRIA (4,7,10)
Days on the floor
TRIA(2,3,5)
Figure 3. Length of stay in the virtual hospital
116
Severe
Patient
Arrival
>
ICU Stay
Moderate
Floor
Stay
No
> Discharged
Figure 4. High level patient workflow
Tamiflu
IV Fluid
Severe
2 doses/day
2 liters/day
Moderate
2 doses/day
2 liters/day
Patient Type
Mechanical Vent
Tube
VA
segments/day
n/a
Figure 5. Daily product consumption by patients (The products considered are typical medical and pharmaceutical
supplies for flu patients.)
117
Daily Demand for Tamiflu
- Demand PI
1403
1000
800
400
rMmrr)^-«inui\Di£>r*r^onoorT>SoO'--"^rsi
D.iyi
Figure 6. Daily demand for Tamiflu
Shipment
/ Delivery
1s
I
>
05:00 /
\
V
~--^Hfr
\
Ordering
Decision
/
Target Levol
\
\
\
\
13:00 \
/ \^
/ +1 day\
N
\
\
>
Ohrs
24hrs
Figure 7. Inventory control model used in the hospital model
118
Medical Supply Inventory Planner
IBi^H
Disease Model | Hospital Model | Cost Model
kappa
• • • Population
i : Size
gamma
delta
Initial
\ \ \ Infected
• • • population
• • |
beta
'
i
'•••••'•
i
i
•
•
•
•
i
•
i
•
Figure 8. Disease Model dialog box of the user interface
Medical Supply Inventory Planner
Disease Model
|-£*|
Hospital Model | cost Model |
Hospitalized
Rate
Days in ICU •
Probability •
Severe
Days on Floor
Probability
Moderate \
to Severe •
Days on Floor
after ICU
Consumption Rate — • • •
Severe Moderate
prod i : I"
Other Settings
Probability • ["
Death
: Prod 2 : r
Other: :
Prod 3 • I"
a-Factor
—:—!„ .fc.,-1 ••»•• 1— •*.. I.II,MV,J^,.,L.IIA-II-J—|—i—i—|—i—i_t—I—I—I—1_
Figure 9. Hospital Model dialog box of the user interface
119
Medical Supply Inventory Planner
Disease Model j Hospital Model
>^A3*
Cost Model |
Holding
Cost
Cm *f tilfti; tm Hit pa ftriti
Shortage
Cost
Ctst Hi
Target Level
Change Cost
i
i
i
M
treatmai dtitbtttl
wmnq
CMI
«f *»J»J target Ml per duap
• i i i i
i
Figure 10. Cost Model dialog box of the user interface
Demand & Target Inv PI
90000
80000
70000
60000
40000 2
o
30000 ""
20000
•Target Inv PI
•Demand PI
-Total Cost PI
10000
0
01
rv
.H
in
CM
m
m
r-ioii^Lnmr-ia)p^Lnroi-icrir~
^•^•Lninr^ooooCTiOrHrvjrsim
Days
Figure 11. Daily demand, total cost per day and the optimized target inventory levels for products 1 in Case 1.
120
Demand & Target Inv P2
90000
1800
80000
70000
60000
50000 Q
40000 iS
o
30000
•Target Inv P2
•Demand P2
-Total Cost P2
20000
10000
iiiiiiiiiniiiiiil
0
rt(MmC<tlflU)NOOflOOIOHNN((l
Days
Figure 12. Daily demand, total cost per day and the optimized target inventory levels for products 2 in Case
Demand & Target Inv P3
70
30000
60
25000
50
s
20000
VI
40
O
15000 ^
re
30
- 10000
20
Demand P3
Total Cost P3
5000
10
0
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121
Demand & Target Inv Tamiflu
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Demand & Target Inv IV Fluid
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122
Demand & Target Inv Mech Vent Tube
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123
TABLES
Table 1. Epidemiological model parameters
Parameters
fi
\IK
My
CFR
8
S(0)
1(0) IN
Description
Transmission rate
Value
0.39
Latent period
Recovery period
4.1 days
1.9 days
Case fatality ratio
Mortality rate
Population size
0.02%
0.0005
100,000
Initially infected fraction of the population
0.002
124
CHAPTER 5
Project 09 06 F: Patient Flow Optimization under Regular and Emergency Hospital
Operations
125
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Patient Flow Optimization under Regular and Emergency Hospital Operations
09 06F
Final Report
Investigator: Mohsen A. Jafari, Ph.D., Department of Industrial and Systems Engineering,
Rutgers, The State University
Email: jafari@rci.rutgers.edu
126
Abstract
The main objective of this project was to evaluate techniques to optimize patient flow
during regular and emergency hospital operations. To achieve this objective, a two-step
methodology was devised: (i) A statistical analysis technique was developed to identify
significant sources of variability in patient flow, (ii) In order to support patient flow optimization
and control, we hypothesized that real time patient and resource tracking will be required. To
that end, a simulation of a typical emergency department at a hospital was built. Our overall
conclusion is that real time information on patient tracking and resource availability can
significantly improve patient flow throughout hospitals. The improvements are expected to be
more significant under surge conditions when traditional tracking and offline techniques are no
longer effective.
However, real time information by itself cannot mitigate the patient flow and safety
issues unless causes of process variations are identified and corrected. RTLS can only be
effective if used in conjunction with a solution platform that controls and optimizes patient flow.
This concept has already proven to be doable and effective in other industries, such as
manufacturing. To cut costs and improve patient safety, it is time to build these solutions for
healthcare delivery systems.
127
Foreword
This project was performed by Dr. Mohsen A. Jafari and was sponsored by the University
Center for Disaster Preparedness and Emergency Response (UCDPER) - A Collaborative
Initiative of Rutgers, The State University of New Jersey, UMDNJ-Robert Wood Johnson
Medical School, and Robert Wood Johnson University Hospital- with support from Department
of Defense Grant No. W9132T-10-1-0001.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
128
Table of Contents
List of Figures
130
List of Tables
130
INTRODUCTION
131
Background
131
Objective
132
Approach
132
Scope
132
Mode of Technology Transfer
132
TECHNICAL REPORT
133
Problem Definition
133
Approach
134
Statistical Analysis of Patient Flow data
134
Real time information and patient flow
137
Emergency Department Simulation Model
140
CONCLUDING CHAPTER
142
Summary
142
Conclusions
142
Recommendations
143
REFERENCES
144
FIGURES
145
TABLES
146
129
List of Figures
Figure 1 - Fishbone diagram of the patient flow variations
145
List of Tables
Table 1 - Simulation Result, Percent Decrease in KPIs with Low Level of Resources
146
Table 2 - Simulation Result, Percent Decrease in KPIs with Low Level of Resources
147
130
INTRODUCTION
Background
Patients' experiences during their hospital visits often involve redundant steps and
procedures leading to unnecessary excessive time, lower quality of service, medical error, higher
cost for patients and hospitals and patient dissatisfaction. The excessive costs are often covered
by the hospitals or paid by individual patients since insurance companies and government run
Medicare or Medicaid have standard payment plans according to pre-defined diagnosis and
treatment procedures. Regardless of who pays for these excessive and unnecessary expenses, the
adverse societal impacts and negative consequences are immense.
The common practice in many hospitals is to use patient flow data to calculate statistics
on key performance indicators (KPIs) and for patient billing purposes. KPIs are used for
reporting and sometimes as aggregate instruments for process improvements. Closed loop
control and monitoring of patient flow patterns using situational awareness capabilities - e.g.
RFID - and feedback loops has not been a common practice in hospitals. But some elements of
this practice has already been proven to be very useful in the Internet online businesses which
utilize their customer shopping habits and patterns to extend their market share and for the
betterment of their services and offerings. While the situational awareness and feedback control
models are more complex for hospitals due to extensive sources of variability and risks involved,
the potential reduction in costs and increase in QoS and patient safety and satisfaction will be too
rewarding to ignore. All these tools become handier especially when the regular normal
operation of hospital is affected by an external incident varying from highway crashes to earthquakes and terrorist attacks. It's in such situations that having a managed patient flow and
131
situational awareness systems - e.g. RFID - can be of great help to the hospital to increase
patient care and lower the number of fatalities.
Objective
This research intends to address the following specific issues:
1. To better understand sources of variability that impact patient flow within hospitals
under normal and surge conditions;
2. To quantify the impact of real time information on streamlining patient flow, and
quantify the value that Real Time Location Services (RTLS) technology can bring to
hospitals.
Approach
Two methodologies used in this research: (i) a statistical data analysis model and (ii) a
simulation based approach. These two methodologies are thoroughly explained in the technical
chapter of this current report.
Scope
The scope of the research and methodologies introduced here include patient flow
analysis throughout hospitals under normal and surge conditions.
The underlying analysis
requires highly granular patient flow data, typically available in Hospital Information System
(HIS).
Mode of Technology Transfer
The results and outcomes of this research are only preliminary and cannot directly
support commercialization. Further development will be required; especially additional analysis
of real hospital data must be conducted.
132
TECHNICAL REPORT
Problem Definition
There are sources of variability that are intrinsic to all health care delivery systems. These
can hardly be avoided, but having efficient management schemes in place can lead to
advantageous outcomes both for patients and hospitals. These sources are: (i) variability due to
patient individual characteristics - this is fully uncontrollable and can frequently cause major
spikes in demand for resources; (ii) variability due to type and severity of disease or medical
services that must be provided to patients; and (iii) variability in capabilities and the level of
professional knowledge that medical staff possess and use under normal, surge and exceptional
conditions. These sources of variability could severely impact patient safety, QoS, professional
satisfaction, and hospital revenue. In this project, we attempt to introduce a data-driven statistical
method to better understand these sources of variations. We will be using standard patient data
which is available at typical HIS.
Additionally, patient safety, quality of care, and hospital revenue are greatly impacted by
the way information and patients flows are synchronized and move across hospitals. Better
synchronization of patient and information flows can significantly reduce the impact of the
above sources of variability. In this project we will address the added value of using RTLS for
this purpose. Many hospitals struggle with this issue in a way that they are not sure if access to
real time information will streamline their existing processes and will eliminate problems
relating to patient flow. As part of this research we attempt to model the use of Radio Frequency
Identification (RFID) by a computer simulation and measure the performance measures for after
and before environment.
133
Approach
Statistical Analysis of Patient Flow data
Our analysis is composed of the following steps:
•
Clustering of patient flow data into homogeneous groups
•
Development of fish bone diagrams to identify variables that contribute to variations
within a cluster and between clusters
•
Statistical feature selection leading to the identification of significant variables from the
list of variables identified by the fish bone diagram.
With clusters and significant variables or factors identified, the hospital management should be
able to optimize patient flow under normal and surge conditions. This is carried out by
associating each new patient with an appropriate cluster, and by managing significant and
controllable variables associated with that cluster. While the control and optimization steps are
not investigated and modeled here, we believe that this methodology has major commercial
potentials and can greatly and positively impact hospital performance under normal and surge
conditions.
Next we present some details of our technical approach.
In a hospital information system, patients are assigned Diagnosis Related Group (DRG)
codes which loosely speaking, show the type of the disease and identify the steps that these
patients must follow while they receive care from the hospital. It is likely for a patient to change
from one DRG to another depending on the outcome of tests and initial diagnosis. It is also
possible for patient to belong to several DRGs. While each DRG groups patients according to
their diagnosis and defines the basis for billing, there may be some significant differences in the
care patterns of patients randomly sampled from the same DRG. In our analysis , sources of
134
variations are categorized into three classes: : (Type I) unique characteristics of each patient
(patient profile), including age, demographics, and other health conditions, (Type II) hospital
resources, including medical staff and major equipments, (Type III) random noise. There are
always un-assignable causes, which are usually grouped under random noise.
Since random
noise is statistically un-controllable, it is imperative to reduce its effect as much as possible. Any
significant reduction on the un-controllable variations will increase "process capability" which
will in turn lead to significant cost reductions.
The statistical method used in this research is a data driven method meaning that it will
use the data extracted from the various data sources in a typical HIS. No private or confidential
data is needed for our analysis. There will be a data node for each patient. Each node consist of
patient identifier; patient profile information such as age, sex, ethnic group; and medical history.
The above node is then associated to a series of medical procedures that are adopted to treat that
certain patient. These series of events or activities is referred to as "sequence" throughout this
report. We used an existing sequence clustering technique to group patient sequences within a
DRG into clusters. The clustering model groups patients within a DRG into groups which are
similar mostly in their treatment pattern and profile information. The variance within a cluster
still exists but is less compared to the variance between clusters.
In order to identify the variables or factors that contribute significantly to the sources of
variations in the patient flow or sequences, we use fishbone diagram also known as Ishikawa
diagram or cause-and-effect diagram. It was first used in the 1940s, and is considered one of the
seven basic tools of quality control. It is known as a fishbone diagram because of its shape,
similar to the side view of a fish skeleton.
135
In this diagram (see Figure 1), causes are usually grouped into major categories to identify
sources of variation. The categories typically include:
•
People: Anyone involved with the process,
•
Methods: How the process is performed and the specific requirements for doing it, such
as policies, procedures, rules, etc.,
•
Machines: Any equipment, tools etc. required to accomplish the job,
•
Materials
•
Measurements: Data generated from the process that are used to evaluate its quality,
•
Environment: The conditions, such as location, time, temperature, and culture in which
the process operates.
Causes can be derived from brainstorming sessions. These groups can then be labeled as
categories of the fishbone. They will typically be one of the traditional categories mentioned
above but may be something unique to the application in a specific case. Figure 1 (Appendix 1)
shows an example of the fishbone diagram for cause and effect analysis of the patient flow
variations in a general hospital.
The next step is to translate these potential causes into random variables. There are two types
of variables:
•
Quantitative or continuous variables,
•
Categorical or discrete variables.
A quantitative variable is naturally measured as a number for which meaningful arithmetic
operations make sense. Examples are height, age, temperature, etc.. Any variable that is not
quantitative is categorical. Categorical variables take a value that is one of several possible
categories. The easiest case is when there are only two classes or categories, such as "success" or
136
"failure," "survived" or "died." These are often represented by a single binary digit or bit as 0 or
1, or else by -1 and 1. When there are more than two categories, several alternatives are
available. Examples are gender, severity of illness, nurse level of expertise, etc..
Using the fishbone diagram and expert opinions, we are able to obtain a pool of potential
variables which can be responsible for the patient flow variations. The next step is to apply a
statistical method to find the most important variables which significantly affect these sequences.
By targeting these variables, the hospital management can significantly improve patient flow. In
order to define the most significant variables we use an existing classifier technique based on
Random Forest {developed by Leo Breiman and Adele Cutler }. It is an ensemble classifier that
consists of many decision trees and outputs the class that is the mode of the class's output by
individual trees. Random forests are becoming increasingly popular in many scientific fields
because they can cope with "small sample sizes and large predictor variables" problems,
complex interactions and even highly correlated predictor variables. Random forest is unexcelled
in accuracy among current algorithms, and runs efficiently on large data bases. Furthermore,
while there is an expectation that all data elements are collected, it is recognized that in certain
situations information may not be available (dates, times, codes, etc.). Random forest has an
effective method for estimating missing data and maintains accuracy when a large proportion of
the data are missing. It also has the capability to give estimates of what variables are important in
the classification which specifically is in our interest in solving the feature selection problem.
For our application the explanatory variables (factors) are classified in two main categories:
•
Patient profile,
•
Hospital resources.
Real time information and patient flow
137
Real time information of whereabouts of patients and availability of resources is essential
to the deployment of optimal patient flow strategies. RFID (Radio Frequency Identification)
technology has proven to be useful in other industries, such as manufacturing and distribution.
RFID technology has also been adopted by some hospitals around the country, but the value
added aspects of RFID in patient tracking is still under debate. It is not clear for many hospitals
if the return of investment on the deployment of RTLS technology is significant. Many hospitals
are not able to weigh the RFID technology costs versus the benefits that they expect from this
technology on streamlining patient flow and increasing patient safety. While there are traditional
means of tracking patients within the hospitals, RTLS (RFID as one example) can track patients
from their point of entry and throughout their hospital experience. Furthermore, the quality of
patient location and availability of resources remains intact when surge conditions occur. (Oranje
et al, 2009)
The second major task in this project is to devise a methodology to quantify value of
RTLS data with respect to the patient flow. We note that RTLS cannot by itself resolve
inefficiencies in patient flow. However, we hypothesize that if RTLS were to be used in
conjunction with an optimal patient flow solution platform, desirable outcomes should be
achievable. A patient tracking system can facilitate the entry time stamps that are not currently
captured. It can also provide real time information on when a patient is available for his/her next
care activity. Such a system can also be integrated into an active compliance process by flagging
patients whose waiting time is approaching stipulated maximums. Tracking of hospital resources
can also help mitigate patient flow issues and reduce the expenditure on the procurement and
maintenance of equipment. Generally speaking, asset invisibility at hospitals leads to the
following problems:
138
• Hospitals over-procure 20-30% of their mobile assets
• Nursing staff spends 10-30% of their time searching for equipment
• Servicing an item takes 8 hours because 75% of the time is spent searching for it
• Assets are not serviced and maintained when required
• Hospitals are having a difficult time complying with the Joint Commission on Accreditation
of Healthcare Organizations (JCAHO) and FDA regulations on equipment maintenance
• Critical staff cannot be located quickly
• Equipment is lost and stolen.
A computer simulation model can help hospital management to investigate the impact of
asset visibility and patient tracking on the hospital's Key Performance Indicators (KPIs).
We developed a computer simulation model of emergency department (ED) in a hospital
and used it to investigate the RTLS impact on patient flow optimization. While the model is
specific to the ED it is modeling, the underlying concept and methodology is general and can be
applied to different EDs, and can also be expanded to cover larger sections of hospitals.
Generally speaking. Simulation modeling allows exploration of the effect of alternative
designs for improving operations by mimicking flows within a system. It allows experimentation
to understand the impact of different scenarios or proposed changes to the system. There are
many simulation language platforms and application that can be used to simulate manufacturing,
transportation, and telecommunication systems. However, developing such models in the
hospital environment is a challenge because of a traditional lack of adequate data. "One of the
most critical parts of any simulation model development is validating the model—comparing the
model's output with the data observed. In order to rely on such comparison one has to make sure
that the incoming data for the model, as well as the data observed for comparison with the model
output, are accurate." The use of Radio Frequency Identification (RFID) technology in hospitals
has the potential to close this gap.
139
Emergency Department Simulation Model
We used Arena software to simulate the patent flow in the ED before and after
implementing RTLS. We refer to these models as basic and alternative models, respectively. The
basic model represents a general emergency department. There are two types of patients admitted
to the ED: urgent and emergent patients. The main difference between these patient types is the
severity of their conditions, i.e., the emergent patients have higher priority compared to the
urgent patients. The resources in the emergency department (ED) are classified in three groups.
The first group, human resources, consists of doctor(s), nurse(s), administrator staff and
mover(s). The second group is equipment. We consider equipment as the assigned resources to
the patient throughout his/her treatment. If an urgent patient arrives, that patient will get a
wheelchair and a designated bed (used only by urgent patients). Likewise, when an emergent
patient arrives, the patient will get a stretcher and a bed for emergent cases. There is a certain
capacity limit for each class of resource. The last group of resources includes machines, such as
X-Ray and EKG machines. They can only serve one patient at a time and they are occupied if the
condition/treatment of the patient requires it.
As we mentioned before, there are two different types of patients arriving to the
emergency department, and they are being taken to different types of treatment programs
according to their conditions. The process flow chart of each patient type is shown in appendix 1.
With the RTLS technology the simulation model changes in two aspects:
1 - Asset management,
2- The flow of patients.
When the mobile assets have RFID tags, the staff knows the exact location of the equipment
at any given time. In the alternative model we assume that wheelchairs and stretchers are RFID
140
tagged. This is in contrast to the basic model where the mover needs to search for the required
equipment. In the alternative model the mover directly goes to the location of the equipment and
picks it up. This change in the model results in search time to be reduced to zero and a non-value
added task to be removed from the process leading to a significant reduction in overall patients'
waiting times.
With patients RFID tagged in the alternative model, patient movements are monitored
throughout ED and its sections. By RFID tagging the caregivers. the model knows whether a
caregiver is available at a given time or not. Having this real-time information, the patients are
directed to the next available unit to receive their treatment instead of waiting in a queue of an
unavailable service. This change in the alternative model facilitates the flow of patients in the
emergency department and alleviates the bottlenecks, which were present in the basic model.
To compare the two models we consider the following performance measures or KPIs:
•
Overall service time: time from the patient's admission to his/her discharge,
Process time: service time excluding the paper work,
•
Time from the patient's admission to his/her settlement on a bed
•
Time of completion of required treatment procedures
In order to further validate the simulation results, we considered two hospital settings:
1- A hospital with low level of hospital resources,
2- A hospital with high level of hospital resources.
Furthermore, to evaluate the performance of the emergency department under different
workloads we considered three settings, by changing the arrival rates of patients:
1 - Low,
141
2- Medium,
3- High.
In total we investigate six scenarios with the above combinations. Table below illustrates the
% change in the above KPIs for these scenarios. Clearly there are significant improvements in
KPIs when RTLS is used at a typical emergency department.
CONCLUDING CHAPTER
Summary
The main objective of this project was to evaluate techniques to optimize patient flow
during regular and emergency hospital operations with a microanalysis of underlying processes
that constitute the elements of patient flow in the emergency room and its surrounding
operations.
To achieve this objective, we devised a two-step methodology: (i) A statistical
analysis technique was developed to identify significant sources of variability in patient flow.
We take the traditional view that excessive variation in any process is not desirable and must be
eliminated, (ii) In order to support patient flow optimization and control, we hypothesized that
real time patient and resource tracking will be required. To that end we built a simulation of a
typical emergency department at a hospital. The simulation takes into account before and after
RTLS scenarios and compares them with respect to some typical KPIs used at hospitals.
Conclusions
To conceptualize step (i) above, we experimented with a single DRG group of patients.
The patients with a single DRG are expected to have similar diagnosis and go through
statistically similar treatment processes. By exploring the sources of variations within this group
and identifying significant factors or variables that contribute to these variations, we are able to
establish decision space for hospital management. The hospital management must then attempt
142
to choose and control some or all of the variables in this space in order to optimize the patient
flow within the hospital. The optimization and control aspects of patient flow was not
investigated in this project. Further research is required.
Simulation experiments from step (ii) above show that RTLS impact is statistically
significant. Major improvements were observed in typical KPIs used by hospitals. While these
results are specific to the ED model used in this project, the methodology is general. Further
research is required to determine if these improvements can be generalized to typical ED and
hospital settings.
Recommendations
Real time information on patient tracking and resource availability can significantly
improve patient flow throughout hospitals. The improvements are expected to be more
significant under surge conditions when traditional tracking and offline techniques are no longer
effective. However, real time information by itself cannot mitigate the patient flow and safety
issues unless causes of process variations are also identified and corrected. RTLS can only be
effective is used in conjunction with a solution platform that control and optimizes patient flow
in accordance to the dynamics of the hospital. This concept has already proven to be doable and
effective in other industries, such as manufacturing. To cut costs and improve patient safety, it is
time to build these solutions for healthcare delivery systems.
143
REFRENCES
Constantin Van Oranje, Rebecca Schnider, Lorenzo Valeri, Anna Marie Vilamoskovam, Evi
Hatazaazdrer, Annalijn Conklin, Study on the requirements and options for Radio Frequency
Identification (RFID) application in healthcare (April 2009 )
144
Figures
Cause and Effect Diagram: Patient Flow Variations
Methods
Patient Factors
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Page 1
Figure 11 - Fishbone diagram of the patient flow variations
145
TABLES
Low Level of Resources
Medium
High
Patients
Patients
Arrival
Arrival
Rate
Rate
9-973
59.659
76.798
24.806
76.345
82.878
51-342
88.120
91.742
64.227
92.466
86.602
Overall Service Time
4.631
55.090
22.257
Process Time
10.381
67.119
23-494
23.672
89.046
32.159
67.484
96.908
89.177
Low
Performance Measure
Patients
Arrival
Rate
Emergent Patient
Overall Service Time
Process Time
Time From ED Admission to his settlement on the
Bed
Time of completion of required parallel procedures
Urgent Patient
Time From ED Admission to his settlement on the
Bed
Time of Completion of Required Parallel Procedures
Table 1 - Simulation Result, Percent Decrease in KPIs with Low Level of Resourc
146
High Level of Resources
Low
Patients
Arrival
Rate
Performance Measure
Medium
High
Patients
Arrival
Rate
Patients
Arrival
Rate
Emergent Patient
Overall Service Time
8.742
6.630
44-671
Process Time
19.178
18.014
65-897
Time From ED Admission to his settlement on the
Bed
47.212
48.802
82.710
Time of completion of required parallel procedures
24.147
48.729
90.625
Overall Service Time
2.226
2.072
8.867
Process Time
5-679
10.180
0.990
Time From ED Admission to his settlement on the
Bed
17.174
31.601
58.202
Time of Completion of Required Parallel Procedures
68.569
72.129
95.161
Urgent Patient
Table 2 - Result, Percent Decrease in KPIs with High Level of Resources
147
CHAPTER 6
Project 09 08 F: Use of Ultrasound in the Emergency Setting to Improve Triage of Trauma
Patients
148
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Use of Ultrasound in the Emergency Setting to Improve Triage of Trauma Patients
Project 09 08 F
Final Report
Investigators:Rajesh Geria, MD, UMDNJ-RWJMS
Email: geriarn@umdnj.edu
149
Abstract
This study evaluated the use of portable ultrasound by pre-hospital providers in the prehospital setting. Ultrasound imaging has been used to rapidly determine presence or absence of
pneumothorax, blood in the abdominal cavity, and appropriate endotracheal tube placement noninvasively. Non-physician providers have used this technology in the advanced setting of the
Emergency Department (ED). This project determined the feasibility of pre-hospital providers
obtaining images in a more austere environment and if the images could be interpreted
appropriately to improve patient care. Further study is needed with more robust equipment to
determine if ultrasound can be used to augment rapid field triage of trauma patients.
150
Foreword
This project was performed by Rajesh Geria, MD and was sponsored by the University
Center for Disaster Preparedness and Emergency Response (UCDPER) - A Collaborative
Initiative of Rutgers, The State University of New Jersey, UMDNJ-Robert Wood Johnson
Medical School, and Robert Wood Johnson University Hospital- with support from Department
of Defense Grant No. W9132T-10-1-0001.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
151
Table of Contents
INTRODUCTION
154
Background
154
Objective
154
Approach
155
155
Location
Scope
155
Mode of Technology Transfer
156
CONCLUDING CHAPTER
156
Summary
156
Conclusion
157
Recommendations
158
Appendix 1
159
152
INTRODUCTION
Background
The Focused Assessment with Sonography for Trauma (FAST) is used to rapidly and
non-invasively assess trauma patients for life-threatening injury in many hospitals across the
globe. However, in the United States, the FAST exam is currently not within the scope of
practice of pre-hospital paramedic personnel. The Emergency Medicine literature has an
abundance of studies demonstrating that when used by adequately trained physicians it can serve
as a risk stratification tool in trauma patients.
We feel this technology can be utilized in the pre-hospital arena, in particular at mass
casualty incidents or the battlefield to help triage and enhance care of victims. Pilot studies
conducted by Heegarard et al. have shown that after brief training sessions consisting of didactic
and hands-on education, paramedics were able to accurately perform these studies on
ambulances over a one-year period. An ultrasound expert at the hospital reviewed all
ultrasounds performed. The data states that 86 FAST exams were performed of which 6 had
positive findings, i.e. hemoperitoneum / hemopericardium. Although this research supports a
growing movement to integrate FAST training into Emergency Medical Services (EMS) scope of
practice, more studies need to be done. We feel paramedics should be first trained in a
controlled setting and have a significant exposure to positive (abnormal) studies prior to being
tested in the field setting. The P.I., Dr. Geria, and paramedics who volunteered their time for the
training executed all work in this study.
Objective
153
We sought to determine if paramedics can accurately perform and interpret the FAST
exam using portable ultrasound technology and a simulation model after brief training from an
emergency physician expert sonographer.
Approach
Paramedics volunteered for a short didactic session followed by hands-on training using a
Zonare (Mountain View, CA) ultrasound machine and Blue Phantom FAST (Redmond, WA)
model (Appendix 1). Each paramedic filled out a pre-study survey relating previous ultrasound
experience and knowledge. They then picked a study number that was written on survey sheet.
The course instructor (PI) was blinded to paramedic number. Volunteers changed fluid states on
the model every week to reflect one of the following scenarios: normal, + hemoperitoneum
RUQ, + hemoperitoneum LUQ, + hemoperitoneum pelvis, + pericardial effusion or combination
of any of the above. Paramedics had the opportunity to practice scanning on the model during
designated times every month. Paramedics scanned the model in a closed room at the EMS
barracks one at a time. They wrote their study number (not their name) and recorded their
findings on a case report form that they placed in a locked drop box in the room. Data was
reviewed by the PI. All paramedics filled out a post-study survey relating to their confidence in
performing the FAST exam and how they envision they would use this in their practice;
specifically a disaster situation or MCI.
Location
Research was conducted at Robert Wood Johnson University Hospital and Robert Wood
Johnson Medical School in New Brunswick/Piscataway. Training was held in the EMS barracks.
Scope
The research was limited to paramedic volunteers who enrolled in the training session.
154
Mode of Technology Transfer
After completion of this project, the Army may choose to train their medics in the FAST
exam and equip field units with portable machines as an additional method for assessing patients
with hidden internal injury in a disaster situation.
CONCLUDING CHAPTER
Summary
23 paramedics participated in the didactic training sessions. The results of the pre-study
survey revealed that none of them had ultrasound training prior to this course and all had at least
4 years of experience as a paramedic. In addition, 35% of the paramedics specifically mentioned
they could envision the FAST exam dramatically changing their practice and improving risk
stratification of patients involved in a MCI.
Three paramedics participated in scanning the FAST model and completed case report
forms. Two paramedics participated in scanning the FAST model on multiple occasions.
Paramedics reported they viewed the online review lecture prior to their scan 5/6 times.
Paramedics manipulated the machine parameters. During their practice scanning
sessions, paramedics reported they needed to adjust the gain to improve image quality 4/6 times.
When they made an adjustment, the gain was increased 4/4 times and during one session they
both increased and decreased the gain. Both sessions where no attempts were made to adjust the
gain were completed by a single paramedic. The location of positive internal fluid was changed
on the model three times.
Paramedics were completely correct in their interpretation of their FAST exam session
3/6 times using intent to treat analysis. In the one session where the paramedic reported they did
not view the online Review lecture prior to the scanning session, they correctly performed and
155
interpreted their FAST exam. In all three examinations with errors, the paramedics had viewed
the Review lecture. For intent to treat analysis, if paramedics identified an internal injury that
was truly present they were considered correct since in real life the identification of an internal
injury would likely impact triage, transport, and treatment decisions. If intent to treat analysis
were not used and case report forms where the paramedic scored the exam as negative and
positive for internal injury, paramedics would have been completely correct 1/6 times.
There were several limitations of this study. A significant delay occurred between the
didactic training session and the actual testing phase due to multiple technical failures of the
scanning model. The model had to be returned to the vendor twice indicating currently available
models may not be robust enough for training in more austere environments such as pre-hospital
provider barracks. Therefore, the paramedics were not able to frequently and quickly practice
what they learned. We attempted to account for this by emailing the lecture reviewing
ultrasound physics and how to perform a FAST exam but could not guarantee all of the
paramedics viewed it prior to testing or that this stop-gap measure was adequate. Next, all of the
paramedics who originally took the training class did not participate in the testing phase. This
could have been because they forgot the skills or lost interest.
Conclusions
Teaching and assessing paramedics skills with portable ultrasound outside of the
emergency department is technically difficult. While using ultrasound models afford paramedics
to test their skills and identify positive findings at rates that would not be reproducible in real life
with real patients, the available technology may be limited by its ability to withstand use in more
austere environments. Further study is needed with more robust equipment to determine if
ultrasound can be used by paramedics to augment rapid field triage of trauma patients.
156
Recommendations
In accordance with American College of Emergency Physicians Emergency Ultrasound
guidelines, emergency physicians must complete at least 25 FAST exams after initial didactic
training in order to be considered proficient. Although there is no hard rule about how many
exams need to be abnormal, it is recommended that at least a small percent should. Although we
have very limited data, preliminary results suggest that paramedics are unable to become
proficient in this skill after only limited training. Although the FAST exam can in theory
dramatically improve risk stratification of patients in a mass casualty incident, further study is
needed to determine if paramedics can correctly identify internal injury with more robust training
models that will allow more practice/evaluation time as well as teaching in more remote
environments. Future skill acquisition may also be augmented by practice on human volunteers
and image interpretation supplemented by using an online video library.
157
Appendix 1
Copyright 2011, Blue Phantom
158
CHAPTER 7
Project 09 10 F: Strengthening Windshield Resistance
159
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Strengthening Windshield Resistance
Project 09 10 F
Final Report
Investigators: Dr. Perumalsamy Balaguru, Department of Civil and Environmental Engineering,
Rutgers, The State University
Email: balaguru@rci.rutgers.edu
Anthony Casale, Rutgers, The State University
Johanna Doukakis, Rutgers, The State University
Stav Ben-Aarosh, MFS Consulting Engineers
160
161
Abstract
The primary objective of this project is to evaluate methods for improving projectile
resistance of response vehicles. These response vehicles are core part of emergency management
because they are needed to transport injured people and medical professionals. Windshields of
vehicles were chosen for experimental evaluation because of their importance for continued
operation even after a possible attack. Review of the current literature of hardening mechanisms
led to the conclusion that attaching high energy films is the most economical way to achieve
projectile resistance. After careful evaluation of all the products available, two film types were
chosen for evaluation: VehicleGARD manufactured by ShatterGARD and a film made by 3M
and distributed from Shore Shield. The evaluation was conducted both at the laboratory and
using actual vehicles. A setup was built to drop a steel ball from various heights. A total six
enhanced windshields were tested in the laboratory and tests were also conducted on three
vehicles. Both systems provide very good resistance in terms of preventing "flying glass". A
careful evaluation of the fractured surfaces leads to the conclusion that VehicleGARD is better
because it provides less spread of cracking. This aspect is important for drivability of vehicles
after the attack.
162
Foreword
This project was performed under the direction of Dr. Perumalsamy Balaguru and was
sponsored by the University Center for Disaster Preparedness and Emergency Response
(UCDPER) - A Collaborative Initiative of Rutgers, The State University of New Jersey,
UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson University Hospital
- with support from Department of Defense Grant No. W9132T-10-1-0001.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
163
TABLE OF CONTENTS
List of Figures
165
List of Tables
165
INTRODUCTION
166
Background
166
Objective
168
Approach
168
EXPERIMENTAL WORK
168
Sample preparation
168
RESULTS AND DISCUSSIONS
170
Importance of adhesion between the windshield and protective film
170
Performance of the films under impact loading
170
CONCLUSION
172
REFERENCES
173
FIGURES
174
TABLES
180
164
LIST OF FIGURES
Figure 1: Preparation of the windshield sample for impact testing
174
Figure 2: Attaching the adhesive side of film to the inside of a windshield
174
Figure 3: Procedure for air bubble removing
175
Figure 4: Impact testing setup in the Lab
175
Figure 5: Impact testing setup in the field
176
Figure 6: The entrapped air bubbles between the film and windshield
176
Figure 7: Failure of the control windshield
177
Figure 8: Cracking of windshield when 3M film used
177
Figure 9: Cracking of windshield when VehicleGARD film used
178
Figure 10: Comparison of the windshields visibility
178
Figure 11: Steel ball impacting at an angle
179
Figure 12: Integrity of the glass when field sample tested at 10 feet
179
LIST OF TABLES
Table 1: Summary of lab results
180
Table 2: Summary of field results
181
165
INTRODUCTION
Since the 1970's automotive windshields have been made of laminated glass to protect
passengers and drivers during normal driving. Laminated glass is strong enough to resist
breaking from high velocity impact by small debris that may be flown into the air by other cars
on the road and therefore ensuring the passengers' safety. It also helps to keep the people away
from potentially fatal lacerations during vehicle accidents. This makes it perfect for its use in
normal highway driving. It has also been used in architectural designs in hurricane prone areas to
protect the people in the building from any large objects that would cause low velocity impacts
such as tree branches or street signs.
Laminated glass is comprised of a layer of poly (vinyl butyral) (PVB) that is adhered to
two plies of soda lime glass on either side. Although laminated glass has been used in both
vehicles and architecture, the design of the windshield and the design of the building element are
very different. A windshield is designed so that both glass plies break in the event a passenger is
thrown into it in an accident. When both plies break, the windshield becomes softer and is more
willing to deform around the person and saves his or her life. If the windshield were to stay hard
and rigid then more harm may come to the passenger.
Apart from the accidents, the windshields can be damaged from many sources, such as
explosions, sudden impacts, fire, etc. In general, to guard from all these sources a protective film
can be applied on the windshields. In this study, two different protective film's performances
were evaluated for the case of emergency response vehicles.
Background
Glass technologies have advanced greatly in recent decades. A material that is so brittle
and yet so important to our daily lives. Glass has been used as a means of art through glass
blowing and staining. It has been used in the advancement of science and technology. It allows
166
us to investigate the objects at a nano-scale level and objects that are far away from the earth
(microscopes, telescopes, etc). Glass has become an essential material in our daily lives. For
example, today glass allows us to correct our vision with corrective lenses, watch the news on
TV, and allows us to enjoy books at night simply by turning on the light. Glass also allows us to
let the sun into our homes and see out of our cars.
The usage of the glass in vehicles calls for a major necessity for the advancement of glass
technologies. Since glass is a very brittle material which can be easily broken, it requires special
technology to increase its strength and ductility. The invention of laminated glass protects us
from major injury or even death through lacerations since it breaks into many small pieces rather
than a few large ones. Laminated glass will not break if it is hit by a small pebble accidentally
tossed by a truck's tire or any other mishap.
Although laminated glass may be very useful for conventional windshields, military and
police vehicles need to protect their passengers from more than just small flying debris. The
windshields for these vehicles must withstand much higher impact loads and even blast loading.
Some inexpensive and innovative materials have been created that can potentially increase the
strength of laminated glass windshields. These materials come in the form of a transparent
protective film that is applied to the windshield.
In general, these films can protect people inside the vehicles against many different types
of hazards such as, UV rays, firearms, hurricane debris, bomb blasts, etc. Many research centers
and commercial companies are constantly developing various kinds of protective films. There
are a number of different films available in the market from various companies such as, 3M,
ShatterGARD, Armorcoat, Apex, ACE Laminates, etc.
In this study, two different films from 3M and ShatterGARD were selected to evaluate
the possibility in using on the windshields of an emergency response vehicles. The company 3M
has been testing their films against a variety of close range explosive charges. Their protective
167
film has demonstrated the ability to retain shattered glass, and to reduce significantly the
potential of glass cut injuries. The other protective film manufactured by ShatterGARD has also
tested their products against not only explosions, but a verity of other impact loads. Their film
holds the glass pieces together, protecting occupants from injury. They claim to eliminate
spalling, which occurs when a bullet hits the glass and becomes a white powder. In this paper,
3M Safety and Security Film and ShatterGARD's VehicleGARD film will be researched and
compared to see which would be best suited to increase in the resistance of the emergency
response vehicle's windshield.
Objective
The objective of this project is to test the increase in the resistance of the emergency
response vehicles' windshields with the use of a protective film. This research will help to ensure
the safety of our response teams during emergencies.
Approach
The impact resistance tests of the windshields were carried out in a lab setting and as well
on real world vehicles based on a steel ball dropping experiment. The details of this work and
results are discussed in the following sections.
EXPERIMENTAL WORK
Sample preparation
The protective film is generally applied on the windshield inside for vehicles. In this
study, first, the windshield was scraped with a razor blade to ensure smoothness of the glass. It
was then cleaned from dust and debris with simple soapy water. After cleaning and drying the
surface, a film piece was then attached. This film piece was cut in such a way that it should be
larger than the windshield size. Before attaching the film, the windshield was once again sprayed
168
with soapy water. The adhesive side of the film was sprayed with same soapy water as in the
case for the windshield. Finally, the film was then carefully placed on the inside of the
windshield with the adhesive side of the film against the cleaned surface of the windshield. The
complete sample preparation process is as shown in Fig.l and Fig.2. The excess film hanging off
the edges of the windshield was then cut off with a blade such that the film almost perfectly fit
the windshield.
Upon application, air bubbles and excess soapy water may be trapped between the
windshield and the protective film. These air bubbles form because the film does not want to
conform to the curvature of the windshield. Air bubbles and soapy water can be removed by
simply pushing them to the edges with a hard plastic scraper. The scraping was performed from
the center of the windshield to the outside edge. Some of the bubbles around the edges needed to
be removed. This can be done by simply scoring the film and overlapping the film on itself or by
cutting small triangles out of the film. The windshields which are free from air bubbles were then
set aside, standing, to cure for two weeks. The application of the film for the field samples was
also done in the same manner. However, the curing time was shortened due to direct sunlight
applied to the cars in field.
Testing Procedure
Lab testing took place in a controlled environment, with all research staff wearing the
proper safety equipment: gloves, eye protection, closed toed footwear. The testing windshield
was placed on the floor with the film-covered surface facing down. To replicate the conditions of
the windshield installed in a car, foam insulation was used as light cushioning so that the
windshield was not sitting solely on the four corners due to its curvature. For the testing, a 2.5
lbs (force) steel ball was dropped from different heights onto the windshield (starting from three
feet and up to nine feet). For this purpose, a quad-pod system was designed, as shown in Fig 4
169
and 5. The steel ball was dropped on two different areas on the windshield, i.e. the center and off
center locations.
The height at which the ball was dropped was increased one foot after every drop for
center impact testing. On the other hand, the off center testing was performed only one time with
the maximum height of center spot testing. Photos were taken of the windshield after every time
the ball was dropped and a video was taken for each drop.
RESULTS AND DISCUSSIONS
Importance of adhesion between the windshield and protective film
The two selected films were attached to the windshields for the impact testing. The
existence of air bubbles between the film and the windshield means that the film was not
touching the windshield (Fig. 6).
Bubbles that formed between the windshield and the film were, at times, difficult to take
out. Sometimes large scoring was needed and large triangular pieces of film were removed to get
rid of the bubbles under the film. This, however, was also counterintuitive since scoring and
removing material also meant that the film would not be reinforcing the windshield to its fullest
capacity. An effort was therefore made to score and remove as little material as possible from the
film (Fig. 3). It was observed that the thicker the film was, the harder it was to remove the
bubbles out from under the film and hence more scoring was needed. The concavity and
roundness of the surface seems to be one of the factors contributing to the creation of bubbles.
The concavity of the windshield also made it very difficult to remove the bubbles and the excess
soapy water out from under the film.
Performance of the films under impact loading
The windshields that were tested in the lab were of the same geometry, support
conditions, and collision angel. The experiment was conducted in such a way that the impact was
170
repeated in the same spot. During the tests, neither the 3M film nor the VehicleGARD failed and
broke on the inside of the windshield whereas the control windshield did, as shown in Fig. 7.
Here the control windshield means which does not have any protective film.
During the testing, the control windshield was very sensitive to the applied impacts. It
was observed that, as the ball increased in height, the cracks got bigger and bigger resulting in
failure of the windshield. A threshold height for the impact tests was chosen to be 9 feet, where
the control windshield completely failed. During the experimental work, 3M films were initially
tested. The first 3M film that was tested was not affected until the steel ball dropped from a
height of 4 feet. However, the cracks increased in size, but the film did not fail even when the
height increased to 9 feet (as shown in Fig. 8). It was observed that when the second 3M was
used, the windshield was not affected until the ball was dropped from 6 feet, and did not fail at a
height of 9 feet same as the first film. Next, when VehicleGARD films were used, initial
cracking on windshield was observed at a height of 5 feet. However, both VehicleGARD films
did not fail at a height of 9 feet either (Fig. 9).
The same experiments were also carried out at 14 feet to examine how the films affect the
visibility of the driver after impact. It was observed that the VehicleGARD film had the best
visibility after impact compared to all the others (Fig. 10).
Thus, it can be observed from the experiments that both protective films can enhance the
strength of the windshield after impacts. However, visibility is better when VehicleGARD is
used. The experimental results are summarized in Table 1 for lab samples.
The field experiments were carried out on actual cars. The impact testing on the cars was
not done in a controlled setting as the lab, but variables were kept as low as possible. Fig. 11
shows the impact testing on a car windshield.
The enhanced windshields did not fail until the steel ball struck 5 times from a height of
10 feet, whereas the control failed immediately after the first strike. This behavior of the
171
enhanced windshields is due to the structural integrity of the glass and the reduction of the crack
propagation. Although the films did not prevent the windshields from cracking, they did prevent
the object and glass shards from entering the vehicle. It was noticed that the exterior glass had a
gash in the film after being struck, however, leaving the film intact in the interior of the car as
shown in Fig. 12. This eventually help the driver and passengers from suffering any major
injuries. It was observed that the energy required for the complete failure of these testing samples
was approximately 210 ft-lbs, this is more than twice of a control sample. The field experiments
are summarized in Table 2.
CONCLUSION
In this study, two different protective films were tested for their suitability in emergency
response situations. The selected films were from 3M and ShatterGARD. Experiment work was
carried out to evaluate the impact resistance and the visibility of the windshields when these
films were applied. The employed steel ball dropping test serves as a promising technique for the
impact resistance test on windshields. The testing samples were prepared carefully in the lab to
represent the actual boundary conditions in the field. Further, the films were also tested on real
car windshields to examine the protective films performance. It was observed that both the tested
films performed better under the given impacts compared to the control sample. The windshield
performance with VehicleGARD was more suitable for visibility after impacts. Since the
visibility is being hampered by the scattered cracks, the vehicle became functionally obsolete
when the 3M film was used. Therefore, VehicleGARD would be the best suitable film for
emergency response vehicles. Further research work is needed to evaluate the significant
difference in the material compositions of these two tested films, and the passengers' safety
issues when the windshields are damaged.
172
REFERENCES
Bousbaa, C., Kolli, M., Madjoubi, M. A., Malou, Z., Mahdaoui, T., Bouaouadja, N. "Damage
survey of a vehicle windshield exposed to sandblasting in Sahara." Physics Procedia, vol
2, no. 3, (November 2009). pp. 1141-1145
http://solutions.3m.com/
http://www.apexwindowfilms.com/
http://www.armorcoat.com/
http://www.shattergard.com/
http://www.smashandgrab.com/
Locker, F. "Stresses in Laminated Glass Subject to Low Velocity Impact." Engineering
Structures, vol 19, no. 10 (1997). pp. 851-56.
Mellor, S. G. "The Relationship of Blast Loading to Death and Injury from Explosion." World
Journal of Surgery, vol 16, no.5 (1992). pp. 893-98.
Ngo, T., P. Mendis, A. Gupta, and J. Ramsey. "Blast Loading and Blast Effects of Structures An Overview." Journal of Statistical Education (2007). pp. 76-91.
Norville, H. Scott, Kim W. King, and Jason L. Swofford."Behavior and Strength of Laminated
Glass." Journal of Engineering Mechanics, vol 124, no.l (1998). pp. 46.
173
FIGURES
Figure. 1 Preparation of the windshield sample for impact testing
Figure 2: Attaching the adhesive side of film to the inside of a windshield
174
Figure 3: Procedure for air bubble removing
Figure 4: Impact testing setup in the lab
175
Figure 5: Impact testing setup in the field
Figure 6: The entrapped air bubbles between the film and windshield
176
Figure 7: Failure of the control windshield
Cracking at 9 feet
IHHBBH^^^^BHBHi
Figure 8: Cracking of windshield when 3M film used
177
fl
Figure 9: Cracking of windshield when VehicleGARD film used
Figure 10: Comparison of the windshields visibility
178
Figure 11: Steel ball impacting at an angle
Figure 12: Integrity of the glass when field sample tested at 10 feet
179
TABLES
Table 1: Summary of lab results
Reinforcement
Type
Control
3M 1
Height and Energy
3ft
7-5 ft-lb
4ft
10 ft-lb
No
Affect
No
Affect
No
Affect
Lateral
crack
down
the
center
5ft
12.5 ft-lb
Bottom
right
corner,
crack 2 ft
from
center
Big spider
crack in
the center
6ft
15 ft-lb
7ft
17.5 ft-lb
8ft
20 ft-lb
Symmetric
crack patter,
Big spider
crack in the
center
large radial
crack
pattern
Dent
forming
Dent
increasing,
Failure
small radial
crack,
increasing
spider crack
increasing
radial
cracks, 1
lateral crack
1.5 ft from
center
Increasing
radial
cracks
Increasing
radial
cracks
3M 1 Off Center
3M2
No
Affect
No
Affect
No Affect
Bottom left
corner crack
2ft from
center
spider crack
and
shattering,
few radial
cracks
Increasing
spider
cracking,
more
defined
radial
cracks
Spider
cracking
Increasing
spider
cracks,
radial
cracks
forming
Increasing
radial
cracks,
Increasing
spider
cracks
Increasing
radial
cracks,
Increasing
spider
cracks
3M 2 Off
Center
VehicleGARD 1
No
Affect
No
Affect
9ft
22.5 ft-lb
Localized
radial
cracks,
Spider
cracking
Increasing
spider
cracking,
more
defined
radial
cracks
Localized
radial
cracks
Breaking
seems to be
localized
VehicleGARD 1
Off Center 1
Deep radial
cracks
VehicleGARD 1
Off Center 2
Deep radial
cracks
VehicleGARD 2
No
Affect
No
Affect
Spider
cracking
Increase
spider
crack, radial
crack starts
to form
More
defined
radial
cracking,
increase
spider
cracking
Large radial
cracks,
increase
spider
cracking
Cracking
seems to
stay
localized
Large radial
and spider
cracks
Large radial
and spider
cracks
VehicleGARD 2
Off Center 1
VehicleGARD
2 Off Center 2
180
Table 2: Summary of field results
Test
Vehicle
Angle of
Windshield
4ft/
10 ftlbs
Control
Ford
Crown
Victoria
LX
64.2°
N/A
VehicleGARD
Nissan
Sentra
66.4°
No
Affect
Mark
3M•
Ford
Taurus
1995
61.9°
No
Affect
Spider
crack
5ft/
12.5 ftlbs
No
Affect
181
6ft/
15 ftlbs
Dent
w/
spider
crack
Mark
Height / Energy
8ft/
7ft/
20 ft17.5 ftlbs
lbs
Bigger
Dent w/
spider
crack
Mark
Spider
crack
w/ dent
9ft/
22.5 ftlbs
10ft/
25 ft-lbs
Smash /
Failure
Bigger
dent
and
crack
5 times
until
Failure
5 times
until
Failure
CHAPTER 8
Project 09 11 P: Building a Decision Support Tool for Studying the Economic Impact of
Loss of Passenger Rail Service: A Prototype of New Jersey's Urban Industrial Corridor
182
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Building a Decision Support Tool for Studying the Economic Impact of Loss of Passenger
Rail Service: A Prototype of New Jersey's Urban Industrial Corridor
Project No. 09 11 P
Final Report
Investigators: Michael Greenberg and Michael Lahr, Edward J. Bloustein School, Rutgers
University, 33 Livingston Avenue, Suite 100, New Brunswick, NJ 08901-1958
Phone: 732-932-4101 ext 673, e-mail: mrg@rci.rutgers.edu.
Michael Lahr's e-mail is lahrfgjrutgers.edu
183
Abstract
We describe options for building economic simulation models that will be used to assess
the regional economic impacts of hazards events on a major rail corridor, and the costs and
benefits of making the system more capable of withstanding events and rebounding from them.
184
Foreword
This research was supported through the University Center for disaster Preparedness and
Emergency Response (UCDPER) - A collaborative Initiative of Rutgers, The State University of
New Jersey, UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson
University Hospital - with funds provided by Department of Defense Grant award W81EWF9223-7361.
We thank Haiyan Zhang, Jinwoo Kwon, John Ottomenelli, and Cory Yemen for their
research assistance.
The opinions, findings, conclusions, or recommendations expressed herein are those of
the authors and do not necessarily represent the views of the Department of Defense and the
individuals acknowledged here.
185
Table of Contents
List of Tables
187
INTRODUCTION
188
Literature and Key Definitions
191
Terrorism and Railroads
191
Economic Impact
192
Data and Methods
195
Study Area
195
Methods of Analysis: Options
197
Applications of CGE Models and Application to Transportation
199
Effects of Transportation Infrastructure on Productivity
202
Gauging the Effect of Changes in Gasoline Consumption
204
Congestion Effects: A Question of Commuter Productivity
206
Disaster Scenarios
208
Scenario 1
208
Scenario 2
208
Economic Impacts of Scenario 1
209
Economic Impacts of Scenario 2
211
REFERENCES
212
APPENDIX: Data Limitations
218
186
List of Tables
Table 1: Economic and Tax Impacts to New Jersey of Loss of Service
to New York Penn State
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INTRODUCTION
For many decades, while nations in Europe and Asia built high speed rail systems, the
United States has built more highways. Only the Amtrak line between Boston and Washington,
D.C. qualifies as a major passenger rail line, and it does not qualify as a high speed line by
standards in Japan, China and parts of Europe. On April 16, 2009, President Barack Obama
released a strategic plan that outlined a vision for high-speed rail in the United States (Federal
Railroad Administration 2009). In his State of the Union speech date on January 27, 2010, the
President mentioned this project.
"We can put Americans to work today building the infrastructure of tomorrow. From the
first railroads to the Interstate Highway System, our nation has always been built to
compete. There's no reason Europe or China should have the fastest trains, or the new
factories that manufacture clean energy products. Tomorrow, I'll visit Tampa, Florida,
where workers will soon break ground on a new high-speed railroad funded by the
Recovery Act. There are projects like that all across this country that will create jobs and
help move our nation's goods, services, and information."
The report and presidential announcements identified ten high-speed rail corridors as
potential recipients of federal funding. Those lines are: California, Pacific Northwest, South
Central, Gulf Coast, Chicago Hub Network, Florida, Southeast, Keystone, Empire, and Northern
New England. Since that announcement and subsequent visits by the President to Florida, a
number of plans have appeared for an even more ambitious set of high speed rail systems. The
US High Speed Rail Association (2011) described a 17,000 mile national high speed rail system
built in four phases. The plan calls for linking the largest cities and the most developed corridors
first. By 2030 it calls for lines that pass through large rural areas, for example from Salt Lake
City through Boise to Seattle. Yet, the Orlando to Tampa, Florida, span—the one visited by
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President Obama after a state of the union speech—was stopped by the Governor of Florida and
others and now may also not be built. No one can say with certainty how much of the vision will
be achieved.
The implementation of this vision has economic, environmental, social, and other
benefits. The promise, however, comes with risks. Rail transit systems are vulnerable to the
plethora of mechanical and human failures that have caused thousands of rail disasters and near
disasters for centuries. In the 21st century, such a system would be vulnerable to a terrorist attack.
In many ways rail networks could make more inviting target than do airways because of the very
many entry and egress points, a lack of passenger screening, and little security around stopped
vessels. Intelligence indicates that the threats to our nation's transit system may be increasing
since 9/11 (Transportation Research Board 1997).
A hazardous event can disrupt service, cause injury, damage or loss of life at the site of
the incident, and cause cascading effects throughout the transportation network, including delays
and economic losses. As dependence on a national rail system increases, so also does the need to
provide measures to protect the systems and to respond as effectively as possible to events. In
short, carrying out a vision of building a world-class network of high-speed passenger rail
corridors will require policy decisions to guide strategic investments to effectively manage
security risks so that passengers can use these upgraded reliable systems in the safest possible
circumstances with risks and vulnerabilities and consequences minimized.
New approaches to incident management and counterterrorism, including both human
and technological processes, will become important components of transit systems. It will
therefore be increasingly important to assess how these solutions impact whole systems (Kappia
2009). Although government and academic researchers have focused attention on prevention
and response at some of the key nodes along the Northeast Corridor and have evaluated specific
technologies (Transportation Research Board 2004), there has been limited research looking at
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the Corridor in large segments, let alone in its entirety, to determine ways to evaluate system
resilience and response strategies so that cost-effective solutions can be discovered to reduce
potential negative impacts.
If, in fact, we do create high -speed corridors, it is essential that we are able to protect the
entire system and provide resilient paths around segments that are already regularly blocked or
that may be likely to become blocked in the future. This is truly an instance where a weak link,
in this case a vulnerable segment, can undermine the entire system: even one vulnerable entry
point (for example, where commuter trains pull into the stations) can cause systemwide failure.
A logical place to test security-related options is the Northeast Corridor (NEC) that runs
over 450 miles from Washington to Boston. The NEC is the most heavily travelled by ridership
and service frequency. For example, more than 1,600 people per minute move through New
York's Penn Station during rush hour (Bushue, 2006).
The purpose of this report is to describe a prototype economic model that would allow
planners to assess economic damage caused by system failures and the benefits and costs of
investments in the system to reduce impacts. Before describing the economic modeling options,
it is important the models are viewed in context. We view rail security as a classical problem in
risk analysis. In order to plan strategically, decision makers should have scientifically grounded
answers to the four basic questions in risk analysis (Kaplan, Garrick 1981, Haimes 2009,
Greenberg 2009).
1. What events can occur?
2. What is the likelihood of those events occurring?
3. What are the consequences of those events?
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4. What investments should be made to prevent intolerable consequences and enable
ecosystems to recover as quickly as possible?
Answering the first two questions in the context of rail security implies understanding
vulnerability and threat, using pre-emptive intelligence and monitoring system state. Once risk
analysts answer the first two of the four questions, then the challenge is to understand the
consequences and to eliminate or reduce them.
The economic model we are building has the capacity to estimate the regional economic
consequences of severe local hazard events and follow the ripples of the events through the
economy. Furthermore, the primary model we selected as our prototype has the capacity to adjust
for changes in the key economic sectors impacted by the hazard event. As described in more
detail below this is a critical attribute. It also has the capacity to assess the economic
consequences of risk management options that could eliminate or reduce the consequences.
Literature and Key Definitions
We briefly discuss consider key elements of the terrorism and economic-impact
literatures.
Terrorism and Railroads
There is a large literature on rail system problems and reliability. The Office of Safety
Analysis of the U.S. Federal Railroad Administration (FRA) maintains a web-list list of every
reported accident since January 1975. The searchable file includes tens of thousands of
accidents, and it is classified into broad categories of collisions, derailments, and other events: it
is further subdivided into train accidents, high-rail grade crossing, and other incidents. The
database includes about 500 different types of events, such as worn rail and defective and
missing crossties. The most frequent are missing and broken crossties, switches, rails, fasteners,
and other elements of the tracks and rail bed. The list also shows accidents related to workers
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failures, vandalism, employees falling asleep, and other human factors. The number of reported
events is also accompanied by a list of direct reportable economic damage. Hence, an analyst can
see the direct economic impacts of washouts of tracks and rail beds, buckled and misaligned
tracks, and failure to comply with signals.
The searchable database can very quickly produce a list of incidents by state for a variety
of incident outcomes. For example, it reports that there were 71 rail-related deaths in New Jersey
in 2001 and that the number gradually declined to 40 in 2009. These data, in turn, can be divided
into categories of who was killed (workers, trespassers, etc.). The FRA also publishes an annual
report that provides raw data and rates and discusses federal government efforts to reduce the
rates.
For those looking for less-imposing documents, Semmens (1994) and Kichenside (1997)
have written books that describe the worst train disasters. For those with less patience, the BBC
(2007) broadcasted a story about the world's 17 worst rail disasters from 1981 through 2007. The
popular literature shows that almost all were in Asia and Africa and includes a range of causes
from brake failures, collisions and derailments to gas explosions beneath two trains to cyclones
toppling a train into a river. Some special studies have been done of terrorist-related events.
Jenkins, Butterworth, and Clair (2010) examined the failed attempt to derail the French high speed train in 1995, and they also examined 181 rail sabotage attempts. This interesting report
provides insights into what terrorists might do.
Economic Impact
By economic impact we mean local, regional, national and international economic
impacts that are direct, indirect and induced. There is no denying that a train station and area
around it can be destroyed by bombs, tornadoes, and other natural and human-initiated events.
Typically estimates of the direct costs of event damage are available within a week to two weeks
of an incident and are widely featured in the media and discussed by elected officials. They
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include human and animal deaths and injuries, severe and moderate damage to structures and
their contents, vehicles, infrastructure, utilities and their delivery systems, landscapes and
agriculture, as well as cleanup and response costs in these local areas (Committee 1999, Mileti
1999, Heinz Center 2000). People in cutoff areas may not be able to go to work and school, and
they may need to leave their homes. Physically handicapped may need assistance (Berube, Katz
2005). The literature has described the difficulties of accurately estimating local impacts
(Committee 1999). Estimating local impacts is essential and difficult to estimate but only the
start of the impact analysis.
County, state, and regional impacts cannot be ignored, especially in the case of an event
that disrupts a rail-corridor event (Committee 1999, Rose Liao 2005, Greenberg et al. 2007, Rose
2004). A rail-related event doubtlessly leads to traffic congestion due to overburdened bridges,
roads, and other impacts on parts of the transportation network. Some people may not be able to
get to work, and some freight may not be delivered or be shifted to other modes. All of these will
lead to reduced sales and as the impact spreads across the landscape. Indirect effects are due to
lost sales as the impacts spread. These nonlocal impacts include declines in sales, wages, and
profits due to loss of function in the areas impacted. Some affected households and businesses
may be located many miles away from the event locale and, especially in the case of a rail
corridor, can be quite extensive. These losses are attributable to reduced supplies and demand
from the affected areas, and slowdowns in transporting products and people.
Induced effects come about when workers lose pay because of the direct and indirect
effects. They buy less, especially of products that they do not immediately need. Government
feels all these impacts because tax collections drop because of business losses and consequent
reductions in worker earnings.
In the short run, the local area may benefit as insurance companies, not-for-profits, and
government from outside of the state or region expend funds in the directly impacted area to
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restore it. Yet, in the worst case for the region, investors could lose confidence in the regions and
withdraw their investments, leading to relocation of economic activity and jobs. Overall,
estimating the spatial spread of impacts is an important objective of economic impact analysis.
Measuring the temporal spread of impacts is critical. The actual life cycle of a serious
event is much longer than the period of active humanitarian, political and economic focus on it.
For example, a derailment in which people are injured and killed may stop train traffic, and if the
railroad administrators believe it is terrorist-related, then all traffic may stop. But repairs to
infrastructure may be relatively inexpensive and quick to repair. In contrast, a bridge or tunnel
collapse in an area with no alternative routes could seriously handicap and area economically and
thereby yield effects that linger for an extended period. It is these economic vulnerabilities that
could undermine a regional economy. It is in such susceptible regions that investors are most
likely to hesitate about spending and perhaps choose not to. Decision makers could be misled
into making unwise decisions about investments, if they are aware only of the short-term
economic costs and benefits, when instead the bulk of the costs are incurred in the short term and
the benefits accrue over a much longer period of time, or vice versa.
When engineered systems like rail lines, water pipelines, gas lines, power grids, dams,
bridges and others fail, Greenberg et al. (2007) note that five managerial failures consistently
raised:
1. to protect engineered systems,
2. to implement land-use planning and design tools to reduce hazards,
3. to provide resources that build resiliency into systems and mitigate against economically
disastrous outcomes,
4. to adequately considered and planned for evacuation/relocation, and
5. to understand the implications of different levels and staging of restoration.
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All five failures are relevant to rail corridors, but numbers 1 and 3 are of particular
interest for this study because there draw attention to policy-significant tradeoff issues. For
example, with respect to Hurricane Katrina, if the Corps of Engineers had spent more to bolster
New Orleans's levees, would it have made much of a difference? What would they have needed
to build the structures to cope with the hurricane? Yet, suppose one of those other locations
suffered a serious event and the money it would have received to protect the location had gone to
the New Orleans levees? Second guessing always follows events, however, but it would be
helpful to at least have proactive analyses that could place costs in the context of potential
consequences for decision makers that must make the tradeoffs. It would also be helpful to know
what the cost would have been to have an evacuation plan that included functioning buses and
other capabilities.
Data and Methods
Study Area
The study area for this pilot project is the State of New Jersey. In 2010, New Jersey has a
population of 8.7 million spread over 7,417 square miles, and the highest population density of
any U.S. state, more than 1,100 people per square mile. That population is spread out over 566
municipal governments with no one of them having more than 280,000 residents. The highest
density of people and commercial activity is along the corridor between New York City in the
northeast and Philadelphia in the west central part of the state. The central tread tying together
this core of dense population is the Northeast Corridor Line.
In 1960, New Jersey was one of seven states with over 36 percent of non-agricultural jobs
in manufacturing. In 1969, manufacturing accounted for 31 percent of non-agricultural jobs in
the state. The vast majority of these jobs were concentrated along this same corridor. But the
state lost 58 percent of its manufacturing jobs between 1969 and 2004. Manufacturing now
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accounts for less than 9 percent of jobs in the state. Only New York State registered a larger
relative decrease (Braham, Anderson, 2001).
In New Jersey, more people now live and work in the suburbs and not along the corridor.
Out of a total of 8.7 million in the year 2010, over 1.0 million New Jersey residents live in a
municipality that abuts this main rail transit corridor and 3.4 million live within 10 miles of it.
Another important demographic characteristic is it is important to note that over 25% of the state
population primarily speaks a language other than English at home, a notably larger proportion
than the US population as a whole. A total of 122,000 people who do not speak English at home
live in a town that borders the corridor, and 290,000 live within 10 miles. This has implications
in the event of a rail corridor or other mast transit hazard event because this population is
disproportionately dependent on mass transit, which implies greater economic consequences for
them.
While we will measure impacts for New Jersey as a whole in this prototype, we will
focus the study on a segment of the Northeast Corridor rail line that is located along the main
line from south to north beginning at the Elizabeth, New Jersey, station through to the southern
terminus of Perm Station in New York. This highly traveled and highly trafficked segment is
15.4 miles long and runs through the most urbanized region in the United States, with two major
bridges and an underground portion that tunnels under the Hudson River and into Manhattan.
The line is used by Amtrak and New Jersey Transit for passenger service and by freight rail
carriers. It operates by electric power with diesel available as a back-up. Further, the nodes
(stations) along this system are intersection points for connecting transit lines operated by New
Jersey Transit in New Jersey and by New York MTA trains at Perm Station, and its busy stations
are filled with thousands of passengers daily who meet other surface transportation vehicles such
as buses and taxis. The prototype economic model will examine the impacts in the counties that
constitute this region (Hudson, Essex, and Union) and New Jersey as a whole.
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Because this segment has examples of most of the infrastructure types that can be found
on this or any other corridor, and its nodes are connection points for numerous linking systems, it
provides a rich laboratory to build and test an economic model that will be useful outside the
immediate study area. The modeling is readily expandable up and down the corridor to other
stations, and is only constrained by availability of data to add depth to the system data base.
Methods of Analysis: Options
The prototype that we have been building for New Jersey will be integrated into a more
comprehensive model that will include all of New Jersey and parts of New York State that
border on New Jersey and are tied to it through the rail corridor. Because this is a prototype, the
example we present later in this report is not based on any single event because we do not have a
final set of hazard events. Indeed, the UCDPER team is, in fact, working a set of events that will
be tested with the final economic model that is built. Before describing the primary model we
picked, we summarize the major options.
Input-output (I-O) models are an obvious choice (Lahr, Stevens 2002, Leontief 1970,
Miller, Blair 2009). They are built from data that describe the interactions of all sectors of the
economy. For example, if one security option was to add more than a thousand concrete barriers,
the 1-0 model would tell us what resources would be required by each business sector from
every other sector, such as steel, concrete, wood, and many others. The authors have an 1-0
model with about 500 economic sectors. Our 1-0 model provides estimates of business
transactions, jobs, earnings, gross state product, and federal, state and local taxes.
1-0 models have advantages and disadvantages. One advantage is that the transactions in
an 1-0 table are relatively easy to understand. A second advantage in the case of our model is
substantial detail by business sector. The major limitations are that the data base used in the US
1-0 models is only updated every five years, and the model provides a single impact estimate
rather than indicating the impacts by year of another convenient time period. Inoperability input197
output models (IIM) overcome the fixed economy assumption by providing estimates of the
change in the most impacted sectors and building it into their estimates (Santos, Haimes 2004,
Haimes et al. 2005a,b)
Econometric time-series models are a second standard economic-estimating package
(Conway 2001). Our econometric model for New Jersey has well over 200 equations that link
changes in the national and state economies based on 25 to 30 years of data. National estimates
of jobs, wages, and prices drive the state estimates. The major advantages of econometric timeseries models are that they are grounded in economic trends and provide estimates on an annual
or other time basis. Unfortunately, if the economy is rapidly changing the trends may lead to
forecasts in the wrong direction. A second disadvantage is that compared to 1-0 models,
econometric models lack the business sector detail. We plant on building an econometric model
for the larger project in order to check the results of the primary model.
Regional economic modeling, Inc. (REMI) is an econometric time-series model that
includes relationships among jobs, income, wages, prices and populations. It adds equations for
interregional trade by industry, migration of labor and households for each region. Accordingly,
REMI, it can be a strong tool to use for projects that impact multiple regions (REMI 1997, Treyz
1993, Greenberg et al. 1999), and are especially valuable when of econometric time-series model
is not readily available. Since we have such a time-series model for the region of focus at the
ready, a REMI model is not needed for this application.
Computable general equilibrium (CGE) models typically begin with an 1-0 model or
slightly modified version of one. The analyst then assumes optimal economic decisions by
producers and consumers in response to markets and prices subject to capital, resource, and labor
constraints (Rose, Liao 2005, Rose, Lim 2002, Rose 2004). A disadvantage of the CGE is their
tendency to reply upon non-regional data for estimating some price elasticities. After
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deliberation and assuring that key elasticities in the study could rely on regional data, we selected
CGE as our model of choice for this exercise.
Applications of CGE Models and Application to Transportation
Because of their versatility, CGE models are a logical choice in many policy applications.
Transportation applications make sense because an outage of a single transportation segment has
system-wide effects. These effects can be observed on individual households and firms as well as
on more aggregate levels—consumers and industries. In many cases deleterious transportation
changes can disrupt production both at the firm and sector levels. Estimating the short-, medium,
and long-term effects of outages of lifelines like a commuter rail system has important
implications. Model estimates can be used to determine who, if anyone, should be compensated
and by how much. It can also be used to identify the potential costs of recovering from a disaster.
But more importantly CGE's may be used in advance of a potential threat to identify reasonable
limits of efforts to mitigate a disaster or at least to improve an economy's resilience in the wake
of a disaster.
An ever broadening CGE model literature shows models to assess the effect of disasters
on economies. Lee and Kim (2005) point out how well suited spatio-temporal models are for
analyzing network losses due to natural disasters. They apply a gravity-type model to a simple
CGE production framework to identify the sectoral distribution of potential losses to an economy
over time. Similarly, Nojima and Sugito (2000) use simulation and incremental assignment
methods to evaluate post-disaster performance of transportation network systems, identifying
vulnerable origin-destination pairs within transportation networks. Sohn et al. (2003) assume
final demand declines and rises in transport costs to estimate the economic impact of Midwest
U.S. floods on a transportation network. In particular they scrutinize intra-zonal flow of
commodities, the modal share of traffic, and the average travel distance on the network to
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determine the transportation network resiliency of economic sectors and further identify critical
segments (weakest links) on the transportation network.
Chang and Nojima (1997) identify two post-disaster measures of highway system
performance: total length of highway open and total connected length of highway open. With
these measures, they attempt to estimate both traffic volumes after the 1995 Kobe, Japan
earthquake and economic impacts of the consequent reductions in transportation system
throughput. In a subsequent paper, Chang and Nojima (1998) applied these measures to compare
events. They used measures of the system's pre-disaster performance to estimate post-disaster
consequences; thereby, they could compare performances of transportation networks across
earthquake disasters and assessed economic activity in relation to transportation volumes. Later,
Chang and Nojima (1999) assessed aggregate transportation system performance, including both
highway and rail networks, to measure economic effects subsequent an earthquake disaster in
Kobe, Japan. They used these performance measures to determine the ability of port facilities to
re-establish services, and the short- and long-term impacts of the disaster on the local, national,
and regional economies. They conducted comparative analyses of system performances in Kobe
(Japan), Loma Prieta (California), and Northridge (California), and demonstrated that
comparisons and assessments may be made in levels of damage, disruption, and restoration
timeframes across systems. Chang (2000) quantifies the lasting economic impacts on Kobe's
container shipping industry, and the long-term economic losses that resulted after the earthquake.
She demonstrates that during the two-year restoration period subsequent the earthquake disaster,
the Port of Kobe lost 20-30 percent of its total volume container cargo to regional competitors
(such as Pusan, Korea; Hong Kong; and Singapore).
Rose et al. (1997) examined the impact of an earthquake disaster on electricity lifeline
disruptions. They developed an approach to estimate economic losses by sector through the use
of economic model simulations of production losses. Van der Veen et al. (2003) looked at the
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total structural economic effects of a flood disaster on households and government, quantifying
production interruption, substitution effects, and both direct and multiplier effects. They
identified three scenarios with varying effects on changes in final demand - ranging from none
to lasting structural changes in the economy.
Interestingly, during the course of the literature search, no article was uncovered that
considered a disaster that focused on a single, specific segment of a commuter rail network and
its subsequent effect on the local economy. Still, the existing literature does provide some
guidance. Sohn et al.'s (2003) focus on the relationship between final demand and transportation
costs is quite instructive, for example. In the case of discontinued transit segment, it suggests
closer examination of fuel consumption (the change in final demand) due to a change in
commute mode. But what is the economic loss? Losses are quite evident in the various works of
Chang and Nojima. In the case of a commuter changes, transportation costs are not just the
change in the costs of transportation service itself, but also the opportunity costs of congestion
time. This raises the question of how such costs are embedded in measureable household and
business transactions so that they can be measured via conventional economic models. The
answer derives from a decision made by the commuting worker and comes down to an answer to
the following question. Does the worker decide to keep pre-disaster work hours and let the added
commute time eat into time that would otherwise be committed to leisure, or does the worker
instead opt to reduce his/her work hours at least somewhat and thereby reduce her/his workplace
productivity?
Given these two building blocks—the economic impact of fuel price rises and the
economic impact of temporary declines in labor productivity—we started to parameterize the
model. We characterized the short-, medium-, and long-term effects of service disruptions to the
rail network by first focusing on the immediate- and long-term effects of gasoline consumption
due to price changes. We then empirically examined the relationship between the labor
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compensation-to-GDP ratio on GDP growth, assuming that the ratio would rise in the wake of
the disaster to be modeled. With these two tools in hand, along with some concept of the size of
the affected labor force and the duration that the rail segment was disabled, we could reasonably
use the prototype to predict the economic impact of rail system disruption.
Effects of Transportation Infrastructure on Productivity
Estimating the short-, medium, and long-term effects of lifeline outages - such as a
commuter rail system - has important implications in determining economic and social losses.
Munnell (1992), by comparing output elasticities of public capital across several studies, found
that as the level of aggregation narrows (from nation to state to metropolis), the [positive]
productivity impact of public infrastructure becomes smaller—even as public infrastructure
continues to demonstrate a significant and positive effect on productivity. Jara-Diaz (1986)
described the relationship between individuals' benefits and transportation processes, showing
that transportation surplus (i.e. lack of congestion) had positive economic effects. Fernald
(1999) examined the relationship between road network capacity and productivity performance,
looking at trends between 1953 and 1989, and addressing the endogenous and spurious
relationships between transportation and productivity. He found that an increase in productivity
nationwide resulted from the "technology shock" of government-provided roads. Further,
Fernald observed that only after the completion of the highway system in 1973 did congestion
become empirically important to productivity. Congestion has negative effects on productivity—
with those industries with greater vehicle intensities experiencing larger productivity slowdowns
due to congestion.
Boarnet (1998) explored the locational effects of changes in a road system, finding that
public capital investment in one district (city, county, state, etc.) attracted additional resources
which seek to benefit from the increased productive capacity, which leads to negative spillovers
in adjacent or comparable districts. Increased output and productivity was observed in districts
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with increased capital investment, thereby drawing a connection between the productive capacity
of a transportation system and output. Baird (2005) discussed the productivity of transportation
infrastructure by looking at contrasting theories on spillover effects: positive spillover effects
theory posits that a transportation network will be more productive if it is part of larger
transportation network, and negative spillover effects theory posits that investments in public
infrastructure in one jurisdiction result in losses of productivity in neighboring jurisdictions—
notably both theories observe a clear link between productivity and transport systems. With the
movement of goods and people as the primary factor in defining mobility, impediments to this
movement (i.e. congestion) compromise productivity.
Van der Veen (2003) identified accounting frameworks (input-output analysis and
computable general equilibrium models) to compute costs of incidents. She also lists change in
consumer surplus (welfare economics) and recovery processes (macro-economics) as ways to
measure the social and economic costs of incidents. Several researchers have devised theoretical
frameworks for building multi-sector computational general equilibrium (CGE) models for
transportation economics (Brocker, Mercenier 2010, Conrad 1997) in order to measure the
economic impacts of changes in capital and transportation flows.
Rose and Liao (2003) used CGE analysis to determine individual and producer responses
to changes in market-prices that result from disaster; their approach assessed the economic
impacts of a disruption in a life-line service, i.e. water supply, to a region. Their analysis
compared results from both 1-0 and CGE models, including CGE indicators of consumer
income, spending, and substitution considerations upon market behavior. Kim et al. (2002) used
an input-output model to estimate flows of goods after a natural disaster, integrating an
econometric model with interregional commodity flow and transportation network models; the
integrated model simultaneously acknowledged highway and railway networks, allocating flows
on the entire transportation network according to elasticities of substitution. Further, the model
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developed by Kim et al. used parameters to determine the performance, costs, and flows for links
- or segments - of the transportation network, thereby allowing for the prioritization of critical
segments.
Thissen (2003) suggested a spatial applied general equilibrium (SAGE) model to
consider indirect economic effects of a terrorist attack on a transportation network. Accounting
for changes in transportation costs, demand, and production, Thissen's model assesses the effects
of changes of transport costs on the labor market, and the subsequent inter-regional distributive
and national generative effects.
While the existing literature shows a growing use of integrated analytical models to
capture the economic costs with respect to changes in transportation infrastructures, as noted
above, it does not assess the effects upon an entire transportation network that result from a
disruption to a specific link—namely the localized effects of increased road congestion
subsequent disruption of rail service. As Fernald (1999) and Baird (2005) showed, congestion
has negative effects on productivity. Using an integrated network model and CGE analysis, we
will show that there are significant implications of time-delays due to congestion, and that
increased levels of traffic on local road networks result from substitution of auto for rail
transport. As Fernald indicates, as road networks become complete in their construction,
congestion increases - as individuals have few or no alternative transportation modes or routes
available other than the existing local road system. As road systems reach capacity and become
saturated, output is reduced. Further, the model shows that the increases in the consumption of
road transport results in increases in fuel costs and time-delays, which together have negative
effects on productivity.
Gauging the Effect of Changes in Gasoline Consumption
We examined the temporal relationship between motor gas prices and motor gas
consumption in both directions: prices increases affecting consumption and consumption
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affecting prices. The purpose of this was to determine how that diversion from rail transit would
inevitably hit New Jersey's road system, either through increased bus service or by a return to
the use of autos. Either way, the loss of rail service would increase the consumption of
petroleum-based fuels, both gasoline and diesel fuel. Subsequently, the rise in fuel consumption
should cause fuel prices to rise, at least in the very short run. In the long run, however,
worldwide effects of OPEC-cartel oil prices would override any short-term effects that a
localized change in consumption would have on local oil prices. From a theoretical perspective
then, any short-run rise in price should feedback to cause fuel consumption to decline at least in
the long run, as commuters adapt, opting either to find another job closer to home, carpool, or
consumption and prices typically should converge to something close to their long-run
equilibriums.
To estimate the elasticity of fuel consumption to its price, we used data for 1978 through
2009 from the Energy Information Administration (EIA) of the U.S. Department of Energy.
Equation (1) shows the results of a simple bivariate time-series regression that was derived.
(1) c = 3340062-p"00'84 -p-0,0149
where
p = Average U.S. retail motor gasoline prices (all grades), dollar per gallon,
c = Total U.S. consumption of finished motor gasoline (thousands of barrels), and
t = time in years.
A Prais-Winsten and Cochrane-Orcutt generalized least squares regression approach was
employed, which corrects for any serially correlated error. The effect of price on demand shows
that a 1.0 percent rise in the price of gasoline results in a decline of gasoline consumption of
205
0.018 percent in the short run. Interestingly longer-term effects (a second year only) almost
double the small short-term effect. But no more consumption affects attenuate beyond the second
year after a change in fuel price. That is all further effects tested were undetectably different
from the null and are not reported here. We also attempted to derive an equation about the
relationship of demand on price. Logically, greater demand should drive up price, but we could
not derive a stable equation, at least partly, we think, because of the aforementioned overarching
role of OPEC on local prices.
In summary, as transport options become reduced and transit commuters resort to roads
for travel, the expected demand for motor gasoline that would result would cause a temporary
spike in gas prices. The spike in prices should cause gas consumption to moderate in the shortterm, but return to usual gasoline demand expectations in the long run. Regardless, long-run
changes in petroleum consumption due to congestion-derived price changes alone are expected
to be small.
Congestion Effects: A Question of Commuter Productivity
As mentioned previously, required diversions from rail transit would undoubtedly
increase usage of New Jersey's road infrastructure, causing congestion on freeways, motorways,
arterials, and surface streets between the homes and workplaces of former rail commuters. We
would expect that all commuters' travel times, not just those of rail commuters, would rise due to
the increased demand on the roadway network. This is because traffic slows and accident
frequencies rise as roads exceed their capacities. In the short-run, this increase in travel time
would leave commuters three possible options: work from home, reduce their leisure time, or
reduce work hours. In the longer run, however, they can change jobs.
While flex-place is a policy in which many firms participate when emergencies arise, the
policy is generally temporary. An exception can be the set of workers who engage in projectbased work activities that are not heavily team-oriented. For the most part, however, heightened
206
congestion is likely to have a negative impact on productivity; either individuals are likely to
become increasingly fatigued at their workplace due to declines in leisure activities or they wind
up spending less time in the workplace, essentially counting time in transit as work hours. To
measure the effects of such productivity decline, we analyzed the relationship between gross
domestic product (GDP) and labor compensation in New Jersey's industries. We used data from
the U.S. Bureau of Economic Analysis of the Department of Commerce and examined the effect
of labor's share of GDP on the GDP yield in New Jersey across 69 industries from 1997 to
2008.2
To examine the relationship, we used a random-effects panel regression approach with
New Jersey's industries as panel variables. As with our examination of fuel consumption, we
examined both short- and intermediate-term effects, but this time GDP is the focus of the
unexpected changes in transportation options and patterns (see Equation 2).
,
„
/
,
\ 1.0058 /
(2)17= 0.987084 (¥,'_,)
, ,
\ -0.8332
(w'jY,1)
where Y[ is the GDP (in $ millions) of industry i in year t and w't is the total industry
compensation for industry i in year t.
The implication of Equation (2) is that for each percentage rise in the compensation /GDP
ratio, GDP falls almost equivalently—by 0.833 percent. Moreover, as no other lagged versions
of the compensation/GDP ratio were able to enter the equation in a statistically significant
fashion, the fall is permanent unless the compensation/GDP ratio itself rebounds. In the case of
our simulations, the ratio rebounds only when the rail lines are back in operation and former rail
commuters return to them.
2
The US BEA data set for GDP by state identifies 81 industries, of which 69 are used in this model due to federal
data disclosure issues.
207
Disaster Scenarios
To investigate the possible economic impacts of a catastrophe occurring to rail transit on
the economy of the State of New Jersey, we generated to basic scenarios. The first is the larger of
the two and emanates from the tunnel into New York City from which full recovery takes about
three years. The second is at Newark's Perm Station from which recovery takes about a year.
Please note that these are hypothetical to illustrate the model.
Scenario 1: In this scenario, a disaster occurs from Newark Penn Station to New York's Penn
Station. All traffic that uses that section of track, including the North River Tunnels, is
discontinued for three years. Post-disaster, all traffic from the south must terminate in Elizabeth,
New Jersey, and only that rolling stock that did not enter the North River Tunnels during the
disaster can be used. Highway bridges, road tunnels, PATH trains, buses, and ferry systems into
New York City operate without disruption. NJ Transit provides buses and shuttle service from
Elizabeth to a nearby PATH station and to the Port Authority's bus terminal to help the usual
80,000 passengers daily to get into New York City.
Scenario 2: In this case, the disaster is much more localized. It is focused on Newark's Penn
Station and some structures in the immediate neighborhood. Track and a temporary station are
quickly built and are functional about a year later. Still, direct rail traffic into New York City
from areas from areas south of Elizabeth is disabled. So all of the alternative transportation
strategies needed for Scenario 1 must be employed, but for a single year only.
On the order of about 80,000-100,000 passengers use alternative means of getting to
work and otherwise visiting New York City and the Meadowlands arenas—the latter are no
longer accessible via Secaucus Station. In the wake of the disasters, workplaces generally
accommodate their workers' commute issues, but at the expense of the firms' profit lines. The
types of companies affected are those that pay their employees enough to enable the longer,
208
more-expensive rail commutes. In Essex and Hudson counties—the core work areas in New
Jersey that would be affected by the altered commuting patterns—jobs of this sort are
concentrated in producer services, which are described best via the following seven industry
titles: Security and commodity brokerage; Insurance carriers; Computer and data processing
services; Advertising; Legal services; Engineering, architectural, and surveying services; and
Accounting, auditing, and bookkeeping, and related services. Almost 60,000 people with an
average pay of about $115,000 are presently employed in these Hudson-Essex industries, and
they account for about 7.6 percent of the 790,000 jobs in the two counties. This set of industries
in these two counties only produces on the order of $15.3 billion (3.1 percent) of the state's total
$478.4 billion in GDP annually. Moreover, Essex and Hudson counties maintain roughly 21.9
percent of the state's total payroll for these industries but only about 17.1 percent of the state's
payroll across all industries. So the region that we have targeted to be most affected by a scenario
for a change in transportation patterns is particularly well endowed with producer services, the
workers of which are likely to have their commutes altered most by the hazard scenarios.
We base our analysis of the productivity consequences of the longer commutes that
result from the disaster on state-based GDP by focusing on the aforementioned producer-service
industries. Applying constraints to this small set of industries simplifies simulations, a necessity
since the modeling process is complex. Still, the industries represent very well the broader group
of sectors likely to be affected by the sort of disasters that are the focus of the study.
Economic Impacts of Scenario 1
In this case, we did not perturb the demand for gasoline that would result through any
heightened increase in the use of road-based transportation. The only long-run effect of fuel
usage is that real prices of gasoline would rise very modestly. That is, our time-series analysis of
the effect of fuel prices on fuel consumption suggests that the rapid rise in gasoline consumption
would relax downward to long-run levels. This likely would occur as households in the long run
209
engage in measures that improve the efficiency of gasoline use: moves closer to workplaces,
changes to workplaces that are closer to home, use of alternative transportation (walking,
bicycle, bus, carpooling), and the use of more fuel-efficient autos.
We did, however, disturb production levels of key producer services in Essex and
Hudson counties. We did this by assuming workers reduced their time at work by 5.6 percent.
The 5.6 percent is obtained by assuming workers on average subtract the 30 minutes of added
daily commute from a typical 9 hour work day. Given that a 1 percent rise in compensation's
share of value added decreases GDP on average by 0.833 percent, a 5.2 percent rise is expected
to cause an annual GDP fall on the order of 4.33 percent in the case of severe Scenario 1. Again
we limited these effects to the selected set of producer services in Hudson and Essex County. We
assumed all area workers in the industries were equally affected.
The 4.33 percent fall in GDP in selected producer services in Essex and Hudson counties
combined would hit the producer service sector rather hard. The 5.6 percent change in the
compensation/GDP ratio alone implies a loss of nearly 10 percent of the industry's profits.
According to Table 1, which presents losses for the peak year of loss only, the Producer service
sector would lose about 10 percent of its jobs (5,708 to be precise) in the long run. That is these
annual losses would be observed in perpetuity. Presumably the losses are incurred by specific
producer service firms, which are already suffering from low profit margins and where workers
have comparatively low rates of pay, e.g., engineering, architectural, and surveying services and
securities and commodities brokers. About an equal number of jobs (another 5,800 jobs) would
be lost in industries other than producer services. Perhaps more devastating to the state, given
present circumstances, is that just over $90 million in state and local tax revenues would be lost
as a result.
The case of federal tax revenues is radically different from that of state and local tax
revenues in the instance of this scenario. They may be lost in the first year, but as the businesses
210
that are losing business realize the durability of the congestion effects caused by the loss of the
rail line, they will exit New Jersey causing the annual losses shown in
Table 1 to continue in perpetuity as was mentioned earlier. The losses to New Jersey are
necessarily a gain to some other geography if not some other set of firms as well. Inasmuch as
the production shifts out of New Jersey but within the U.S., the federal tax revenues posited in
Table 1 will, therefore, not be lost to the nation. Rather Table 1 suggests simply that they not be
credited as having been generated by firms in New Jersey.
Economic Impacts of Scenario 2
As suggested in the formulation of this scenario, the basic direct effects are essentially
the same. That is, the commutes for the same number and distribution of workers are disrupted
through the disabling of the Northeast Corridor rail line from Elizabeth to Manhattan. The
difference is strictly in the duration of the event. In this case, impacts that result from the event
last a single year during which the line is repaired and after which it is operating. Note in this
case and in the case of a long-run disruption, the reconstruction effects are not included. This is
strictly because they are not easy to estimate without more precision in the scenarios. They
would be included in a more-detailed study
In the case of the long-run scenario, Table 1 showed the peak annual long-run losses that
would be achieved in perpetuity. In the case of an event that curtails commutes by rail on the key
section of the Northeast Corridor Line for a single-year, Table 1 shows the totality of the
impacts. That is the losses are incurred by the businesses for the year, but with the promise of the
resumption of rail service operations and the clearing up of the congestion that resulted from its
absence, affected business immediately rebound. That need not be the case. Some business might
choose to relocate away from unreliable rail service, which would be costly. This illustration
does not speak to those possibilities.
211
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217
APPENDIX: Data Limitations
Additional data that would allow us to add nuances to our economic forecasting models.
Business revenue losses by detailed industrial categories due strictly to the lack of productive
operation during the postulated failures will be estimated by assuming that national inter-industry
relationships prevail in New Jersey. Losses due to the disruption of other systems as a result of
the loss of rail service were not possible to pin down because pertinent critical data do not exist
for the study area. However, we do capture some of these losses indirectly in the model
transaction table. For example, business losses that will be incurred from disruptions in the
delivery of food are captured by the links between transportation and the food industry. All of
these are captured implicitly in part in the models because impacts of a loss of rail service impact
many businesses. But an explicit capability to capture these transactions requires field
investigations before they can be incorporated into models. Given the time and resources
available, it was not feasible to conduct extensive field research, and even with time, some of the
data may not be possible to collect because of security issues.
218
Table 1: Economic and Tax Impacts to New Jersey
of Loss of Service to New York Penn Station
(5.6% Rise in Compensation/GDP Ratio, all impacts are actually negative)
Economic i Component
Income
Employment
(jobs)
(000$)
Output
(000 $)
I.
1.
2.
3.
4.
5.
6.
7.
TOTAL EFFECTS
Agriculture
Agri. Serv., Forestry, & Fish
Mining
Construction
Manufacturing
Transport. & Public Utilities
Wholesale
1,269.4
782.3
6
21
2
46
185
294
8. Retail Trade
9. Finance, Ins., & Real Estate
1,547.2
18,865.4
45,739.9
86,083.9
33,883.4
121,063.7
788,748.2
226
1,892
2,800
10. Services
11. Government
966,665.3
13,331.9
6,035
98
2,077,980.8
Total Effects
II. DISTRIBUTION OF EFFECTS/MULTIPLIER
1,314,146.7
1. Direct Effects
763,834.1
2. Indirect and Induced Effects
2,077,980.8
3. Total Effects
11,605
1.581
4. Multipliers (3/1)
III. COMPOSITION OF GROSS STATE PRODUCT
1. Wages—Net of Taxes
2. Taxes
a. Local
b. State
c. Federal
5,792
5,813
11,605
2.004
Gross Domestic
Product (000$)
167.8
405.7
223.7
2,504.4
10,215.9
22,664.4
13,778.8
45,961.6
833.5
6,340.9
11,498.5
35,505.9
340,364.9
423,778.9
423,927.0
6,048.6
866,262.7
468,770.2
7,349.8
591,370.3
274,892.4
866,262.7
1.465
338.9
647.9
14,553.1
70,061.2
1,039.678.8
627,536.8
404,792.3
1,032,329.0
1.645
798.178.73
160,314.09
26,258.15
22,980.47
111,075.46
18,845.39
General
Social Security
92,230.07
79,813.86
3. Profits, dividends, rents, and other
1,032,329.05
4. Total Gross State Product (1+2+3)
IV. TAX ACCOUNTS
1. Income --Net of Taxes
2. Taxes
a. Local
b. State
c. Federal
General
Business
798,178.73
Household
160,314.09
26,258.15
22,980.47
111,075.46
18,845.39
174,540.09
22,365.96
19,587.74
132,586.39
132,586.39
0
92,230.07
Social Security
EFFECTS PER MILLION DOLLARS OF INITIAL EXPENDITURE
Employment (Jobs)
Total
817,217.96
334,854.16
48,624.11
42,568.19
243.661.85
151,431.78
92,230.07
8.5
659,182.7
Income
32,392.3
State Taxes
Local Taxes
Gross State Product
37,000.5
785,550.9
219
CHAPTER 9
Project 09 12 P: Intelligent Demand Assigned Networks Cost and Performance
220
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Intelligent Demand Assigned Networks Cost and Performance
Project 09 12 P
Final Report
Investigator: Dr. Mohsen Garabaglu, Systemic Concepts LLC.
Email: systemic.concepts@gmail.edu
221
Abstract
This project studied state of the art satellite network architectures and technology. At any
catastrophic event, communications networks play a key role in the effective management of the
catastrophe and dissemination of vital information. Existing means of communications such as
public switched telephone networks, cellular telephone networks and computer networks could
be damaged, destroyed or over-loaded with heavy traffic caused by the emergency condition.
Deploying wireless communications is typically among the first priorities in any emergency
response, rescue and relief situation. However, terrestrial wireless networks (cellular and land
mobile radios) are only useful when communications towers and other infrastructure systems are
in place to connect wireless equipment to the local and global communications backbone.
Satellite networks are the only wireless communications infrastructure that is not susceptible to
damage from disasters. Satellite communications networks, due to its technology, inherently are
immune from catastrophic events and can play a key role in providing an effective emergency
management and disaster recovery network. Designing a cost effective and reliable EM/DR
network architecture suitable for data, VoIP, video and content streaming applications, is crucial.
It is concluded that an optimal and cost-effective solution is based on concepts of cloud
communications and intelligent network architecture.
222
Foreword
This project was sponsored by the University Center for Disaster Preparedness and
Emergency Response (UCDPER) - A Collaborative Initiative of Rutgers, The State University of
New Jersey, UMDNJ-Robert Wood Johnson Medical School, and Robert Wood Johnson
University Hospital - with support from Department of Defense Grant No. W9132T-10-1-0001.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
223
Table of Contents
List of Figures
226
List of Tables
227
1.0 INTRODUCTION
228
1.1 PHASES OF DISASTER MANAGEMENT
229
PREPAREDNESS
230
RESPONSES
230
RECOVERY
232
RECONSTRUCTION
232
BENEFITS OF SATELLITE SOLUTIONS
233
1.2 SYSTEMS OVERVIEW
233
1.3 SATELLITE LINK AVAILABILITY
235
1.4 MOBILE AND TRANSPORTABLE TERMINALS
236
1.5 POWER SYSTEMS AND LOGISTICS
238
1.6 COMMUNITY SERVICES
239
1.7 NETWORK SECURITY
239
1.8 HETEROGENEITY AND NETWORK INTEGRATION
239
1.9 RESOURCE TRACKING AND LOCATION AWARENESS
APPLICATIONS
240
1.10 SENSOR DEVICE NETWORK
241
1.11 TYPICAL DEMAND ASSIGNED NETWORKS
241
2.0 INTELLIGENT NETWORKS
243
2.1.0 CLOUD COMMUNICATIONS
244
2.1.1 BASIC CONCEPT
245
224
2.2 CLOUD ENGINEERING
247
2.3 CHANNEL ACCESS METHODS
250
2.3.1 FREQUENCY DIVISION MULTIPLE ACCESS (FDMA)
251
2.3.2 TIME DIVISION MULTIPLE ACCESS (TDMA)
251
2.3.3 CODE DIVISION MULTIPLE ACCESS (CDMA)
252
2.3.4 SPACE DIVISION MULTIPLE ACCESS (SDMA)
253
2.3.5 CLOUD NETWORK ACCESS METHOD
253
2.4 QUALITY of SERVICE (QoS)
254
2.5 TRANSMISSION EFICIENCY
255
2.5.1 DVB-S2 PERFORMANCE
258
2.6 RADIO RESOURCE MANAGEMENT SYSTEM
258
2.7 STATE OF INDUSTRY
259
3.0 BUSINESS MODELS COMPARISON
263
3.1 LEGACY NETWORK MODEL
264
3.2 CLOUD NETWORK MODEL
264
4.0 COST ANALYSIS
266
5.0 CONCLUSION
266
REFERENCES
268
FIGURES
269
TABLES
282
225
List of Figures
Figure 1. A simple satellite link
269
Figure 2. Typical North America Ku-band satellite footprint
269
Figure 3. Typical earth station
270
Figure 4. Eb/No Values for various BER performances
271
Figure 5. Typical mobile unit
272
Figure 6. Typical transportable /Flyaway unit with carrying case
272
Figure 7. A typical backpack satellite terminal
273
Figure 8. A typical use of satellite imagery and asset tracking data
273
Figure 9. DR/EM Wireless sensor device network illustration
274
Figure 10. Typical legacy demand assigned network
274
Figure 11. Legacy demand assigned network connectivity scheme
275
Figure 12. Cloud communications concept
276
Figure 13. Network virtualization illustration (Cisco Networks)
276
Figure 14. Network virtualization functional areas (Cisco Networks)
277
Figure 15. Access Methods Illustration
278
Figure 16. GQoS model structure illustration
279
Figure 17. DVB-S2 Modulation Constellations
280
Figure 18. Legacy network model illustration
281
Figure 19. Cloud network model illustration
281
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List of Tables
Table 1.DVB-S2FEC Rates
282
Table 2. DVB-S2 Throughput for 36 MHz Transponder
282
Table 3. DVB-S2 Spectrum efficiency versus required C/N on AWGN channel
283
Table 4. Legacy networks specifications summary
284
Table 5. Cloud network capacity estimate
285
Table 6. Link Analysis Summary
286
Table 7. Networks cost summary
287
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1.0 INTRODUCTION
As noted, the inherent advantages associated with the satellite technology make these
networks an ideal choice for DR and EM applications. International Telecommunications Union
(ITU), the regulatory body of the world, recognizes the important role of satellite technology and
its benefits in EM/DR situations with the following statement:
"Satellite transmissions using small aperture earth stations, i.e. fixed VSATs, Vehicle-Mounted
Earth Stations (VMES) and transportable earth stations, are one of the most viable solutions to
provide emergency telecommunication services for relief operations. These Fixed Service
Satellites (FSS) systems are extremely effective in providing emergency telecommunication
services for relief operations, as they are inherently suitable for data delivery and quick
deployment. FSS can also be effectively utilized for early warning operations, including
earthquakes and tsunamis " says Dr. Hamadoun Toure, ITU Secretary General.[l]
Although the satellite technology is greatly suitable for disaster recovery and emergency
communications, the employed technology usually is not be up-to-date therefore resulting in
inefficient utilization of expensive satellite resources (transponder power and bandwidth). Using
state of the art technologies coupled with a suitable network architecture one can reduce network
operation cost drastically while improving network overall performance. To achieve this
objective, this study examines the next generation intelligent satellite EM/DR networks with
emphasis on the cloud communications. In addition, certain network engineering and design
issues that could result in a high degree of cost containments are studied within the framework of
cloud communications. In general, following specific areas are studied in connection with
EM/DR networks applications and cost containment.
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1. Cloud communications satellite networks concept
2. Service-Oriented Architecture (cloud engineering)
3. Advanced demand assigned techniques
4. Advanced Quality of Service (QoS) protocols
5. Transmission coding techniques
6. Intelligent Radio Resource Management (RRM) systems
In addition, certain features and issues critical to EM/DR networks, including network
heterogeneity, network security issues, wireless sensor application support, location awareness
capability are discussed. Regarding the content structure, this document includes the following
sections:
1. An introduction to DR/EM planning and satellite networks
2. Architectural design for intelligent demand assigned networks
3. Comparative cost/performance analysis of intelligent networks vs. typical legacy networks
4. Cost Containment business model
In order to better understand system tradeoffs and network architecture, certain design,
engineering and disaster planning issues also are reviewed in following subsections.
1.1 PHASES OF DISASTER MANAGEMENT
When examining the communications requirements for a disaster management scenario, it is
important to define the challenges and needs of each phase of the disaster. Depending on the
phase in which the disaster relief efforts are in, the communication requirements may change.
This requires a flexible and scalable solution to support all essential communications
applications. Typical applicable phases of a disaster management for communication needs may
involve the followings [2]:
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•
Preparedness
•
Response
•
Recovery
•
Reconstruction
Organizations and agencies shall develop emergency management plans and be prepared to
respond to a disaster as quickly as possible. First Responders shall be equipped to optimal
mobile solution that can be easily deployed and quickly establishes the first lines of
communication. During the recovery and reconstruction period, communications network plays
an important role in connecting the field staff to their central offices and/or EM centers.
PREPAREDNESS;
When a disaster strikes, immediate actions can be taken if relief agencies and aid
organizations have prepared their EM/DR contingency plans, outlined their communications
needs and coordinated any involved multi-agency activities. It is important to obtain all
necessary permits, licenses and approvals from the government authorities that might be required
for communications network operation. Preparedness can also include using wireless sensor
networks for real-time warning purposes if applicable. For example, remote terminals of seismic
stations, bottom pressure recorders and tide gauges sensors can detect earthquakes and tsunamis,
and report the collected data to EM centers in a real-time basis using satellite network. All
component of the network shall be routinely monitored and tested to insure a functional and
operational network during the emergency. Emergency power systems, handheld
communications equipment, network security systems and interagency interoperability also shall
be periodically examined and tested.
RESPONSES:
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Generally, the first response effort usually takes up to a couple of weeks after the disaster
strikes and is characterized by small teams exploring the area to discover the state of the
survivors and infrastructural damages. This phase involves mostly search and rescue work and
fulfillment of emergency services and basic humanitarian needs. First responders face many
obstacles during this phase and keeping two-way communication is critical between response
teams and their command centers. Heavily damaged or destroyed terrestrial networks often
throw entire regions into a complete communications blackout. Even if a part of the existing
infrastructure is operational, available lines quickly become oversubscribed by heavy traffic
volume, making communications through them intermittent or impossible. In either case,
committed bi-directional communications is required to coordinate relief efforts across wide
geographic areas where quick response times are the key to success. The communications
systems used during this phase must ideally meet a range of requirements as:
•
High mobility communications for both EM vehicles, ambulances and relief personnel
•
Easy to transport
•
Quick to deploy independent of geographical location
•
Bypass traditional terrestrial networks
•
Easily scalable to meet growing needs during the relief effort
•
Offer user-friendly configuration and network management
•
Highly reliable and easy to maintain with little technical expertise in the field
•
Support high bandwidth for any mix of voice, data and video applications
•
Require minimal power and can operate with alternative source of power
•
Ensure data security with built-in encryption
•
Support of wireless sensor communications
•
Location awareness capability
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•
Support central network management
RECOVERY:
After the immediate danger and basic needs have been addressed, recovery efforts will focus
on maintaining important infrastructures, semi-permanent accommodations, temporary offices,
hospitals and medical centers to aid victims. The communication capabilities will need to scale
up quickly and become more permanent while mobile groups will still need portable
communications and power to provide service to other locations within the disaster area. The
recovery applications may involve administrative work, voice calls, multicasting and video
distributions, assessments of relief supplies, assessment of the damages and telemedicine data.
Higher speed communications with enhanced application support are needed for this stage of the
disaster including:
•
Supporting higher speeds for more sophisticated applications such as VPN, multicasting,
telemedicine, large file transfers, VoIP, internet browsing, emails
•
Advanced IP Routing
•
Security for sensitive information and transactions
•
Quality of Service (QoS) management
•
Advanced network management capabilities
•
Easy reconfiguration and simple operation
•
Multi-network interface and heterogeneity
RECONSTRUCTION:
As permanent development and reconstruction begin, the relief agencies and constructions
entities requirements for communications become more formalized and complex. As
reconstruction can take years to be completed, the communication network will need to grow to
support more workers and more business critical applications like videoconferencing, VPN, file
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transfer, VoIP and Internet access. A hierarchy of offices will develop and a flexible
infrastructure must be maintained to accommodate offices that are more permanent and field
offices that still need portability. For this phase, the type of applications normally remain the
same; only the size of the bandwidth and equipment required will have to scale up to support the
increased communication needs.
BENEFITS OF SATELLITE SOLUTIONS:
Success in emergency relief and disaster recovery is measured by quick response times and
the ability to establish real-time connectivity. Satellite communication networks are quickly
deployable and provide the backbone for the rescue and support initiatives during time of crisis.
Field tested emergency relief and disaster recovery satellite solutions provide immediate
communications even in an inhospitable environment. Using global satellite networks, the first
response, medical or any emergency team will have full communications capabilities with voice,
data and video, whether the emergency team is in a densely populated urban area where the
infrastructure is damaged, or a remote and isolated location where no infrastructure exists.
Intelligent platforms discussed in this study can integrate various advanced technologies and
support complex applications as noted above for various phases of a disaster scenario.
The modular nature and inherent scalability of the satellite systems delivers maximum
flexibility to anticipate diverse operational and technical needs regardless of bandwidth
requirements, application, and frequency band or network topology.
1.2 SYSTEMS OVERVIEW
Overall, a satellite network includes two major components; a) the communications satellite
or space segment, and b) the earth stations or ground segments. A geostationary
communications satellite (space segment) located at the altitude of 22,236 miles may operate at
different frequency bands such as C-band, Ku-Band or Ka-Band to name a few. Each satellite
may have some number of transponders with defined amount of power and bandwidth. Each
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transponder receives transmitted signals from the earth stations and re-transmits those back to the
earth stations again. For some technical and engineering reasons, most DR and EM networks
utilize fixed service Ku-band satellites (FSS) with 11.7 to 12.2 GHz downlink frequency band
and 14.0 to 14.5 GHz uplink frequency band in North America region. Therefore, we consider a
Ku-Band satellite for our tradeoff analysis in this project. A simple satellite link is illustrated in
Figure-1.
A typical North America footprint of a Ku-band satellite is presented in Figure-2. Key
subsystems of a typical earth station including the RF system and satellite modem are illustrated
in Figure-3.
A satellite modem (modulator and demodulator) as shown in Figure-3, is utilized to establish
the communications link between two earth stations using the satellite as a relay station in the
space. The word "modem" stands for "modulator-demodulator". Satellite modems' main
function is to transform an input bit stream to a radio signal and vice versa by employing certain
modulation, demodulation, encoding and decoding techniques. Quadrature Phase Shift Keying
(QPSK) and 8-Phase Shift Keying (8PSK), among others, are modulation schemes more often
used to establish a satellite link between two earth station terminals.
Further, Forward Error Correction (FEC) coding schemes are used to reduce the link bit error
rate. The FEC schemes may include 1/2, 2/3, 3/4, 5/6, and 7/8 coding rates just to mention a
few. Beside the design and engineering factors of the satellite modem, a link performance is
directly related to Eb/No value of the received signal at the input of the demodulator. Eb/No, the
energy per bit to noise power spectral density ratio, is an important parameter in digital
communications and data transmission. It is a normalized signal-to-noise ratio (SNR) measure,
also known as the "SNR per bit". It is especially useful when comparing the bit error rate (BER)
performance of different digital modulation schemes without taking bandwidth into account.
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Eb/No is closely related to the carrier-to-noise ratio (CNR or C/N), i.e. the signal-to-noise ratio
(SNR) of the received signal, after the receiver filter but before detection:
C/N = Eb/N0 • ^
Where:
fb is the channel data rate (net bit rate), and
B is the channel bandwidth
The required Eb/No value at the input of a satellite modem is an important factor for a
satellite link design and it is usually optimized in terms of modulation scheme, FEC coding,
desired link availability and the satellite modem performance. Required Eb/No values for certain
BER performance are provided in Figure-4.
However, a satellite modem is not the only device needed to establish a communication
channel. Other equipment that are essential for creating a satellite link includes satellite antennas,
low noise amplifiers, power amplifier and frequency converters collectively called RF System.
In addition, the noise and inter-modulation characteristics of the RF system is an essential factor
in overall satellite link performance.
1.3 SATELLITE LINK AVAILABILITY
In telecommunications, the term availability is defined as the period of the time that the
systems or communications channel performs as per defined specifications. Mathematically,
availability is expressed as percent of the time that system performs according to the committed
specifications. For example, 99.5% availability for a satellite link means that link could be down
and nonfunctional for 0.5% of the time i.e. 43.8 hours per year. Many factors could result in
link unavailability including the link signal attenuation due to rainfall. Heavy rainfall could
attenuate the radio signal causing interruption in the satellite link. Depending to required link
availability, appropriate rain margins should be included in the link budget to prevent link
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outages due to rain attenuation. Certain intelligent satellite modems, as it will be discussed later,
are designed to detect the rain fade and increase the transmitted signal level to compensate for
the occurred signal attenuation. Since EM/DR networks shall be operational under severe
conditions, necessary margins shall be included in system design criteria to guarantee minimum
99.5% network availability under severe climate operation conditions.
1.4 MOBILE AND TRANSPORTABLE TERMINALS
Mobile communications (vehicular and helicopter) shall be included in the network design
to provide connectivity between ambulances and first responders vehicles and the EMC. This
shall be done by utilizing automatic antenna pointing systems coupled with a GPS unit. Satellite
link shall be designed to accommodate required data link capacity with appropriate link margin
for voice, data and video streaming in a mobile environment. It is important to distinguish
transportable units with the mobile units. In this project mobile units apply to the vehicular
terminals with broad communications capability with EMC.
In addition to mobile units, unusually EM/DR networks shall include some transportable
satellite terminals. These terminals shall be designed with appropriate antenna size and antenna
alignment tools for quick deployment and ease of packing and transportation.
Some features of transportable terminals may include:
Light weight carbon fiber construction
Rapid deployment in less than 5 minutes
Secure voice, data and video communications
X, Ku and Ka Band RF system
Modular design with field replaceable cartridge assemblies
Auto-acquisition, Satellite ID and tracking
Modem agnostic
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•
Quick-connect cartridge feed for RF line replacement unit
For greater portability and improved ease of use, tactical backpack satellite terminals with
auto-pointing feature is now available with up to 1 Mb data transfer rate for secure voice, data
and video communications. A self-contained and self-powering backpack with satellite,
wireless, and radio-bridging capabilities provides immediate communications for a small team in
extreme first-mile environments where there may be no roads, power, or any other means of
communications. These terminals are designed for rapid assessments, command and control, and
far-forward incident and disaster processes. These can also be used as a concealed
communications platform for the transmission of unmanned ground sensor data. Its redundant
systems and satellite-based Broadband Global Area Network (BGAN) ensure connectivity even
in the most unpredictable settings. Some EM/DR applications for the backpack terminals may
include:
1. Real-time local and remote collaboration
2. At-a-glance situational awareness
3.
Smart alerts for incident data and looming secondary threats
4. Fusion of data from environmental, biometric, and security sensors
5. Quick access to subject-matter experts and other recovery resources
6. Instant language interpretation and translation
7. Location identification and awareness capability
And some backpack terminal features may include the followings:
•
Secure, wireless local networking (Wi-Fi 802.1 lb/g)
•
Integrated broadband wireless (EVDO/3G)
•
Streaming and burst IP satellite connectivity (BGAN terminal/Inmarsat network)
•
Solar power generation to run all components and for excess energy storage
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•
Methanol fuel cell (optional) for power generation when solar is unavailable
•
Specialized Lithium Ion (Li) power storage unit with ultra-pulse capacitor for optimized
charge and discharge
•
Incident Commanders' Radio Interface• interoperable radio bridge connects municipal
public safety radios, state and federal radios, and telephones
•
Options for VoIP telephony for use on the CommsPack network
•
Satellite telephony
•
Satellite connectivity and wireless capabilities for voice, video, and data
•
Land mobile radios and interoperability solutions
•
Independent power systems
•
Streaming video, videoconferencing and surveillance
•
Global location awareness support system
Typical Mobile, transportable and backpack terminals are illustrated in Figure 5, 6 and 7
respectively.
The most critical issue for an EM/DR network is the network availability and preparedness
at the time of a disaster. This means that the network and all its components shall be functional
all the time and entire network shall be monitored and tested routinely to insure specified
network performance. Self-diagnostic and fault detection is readily available in many
communications network including the satellite systems. Basically, the EM/DR network shall be
turned on to function in an idle mode so the system can routinely monitor its health status.
1.5 POWER SYSTEMS AND LOGISTICS
In general, contingency planning and disaster preparation measure, including pre-positioning
of emergency supplies and logistics, handheld communications equipment and power generators
are crucial and can greatly reduce the impact of a disaster. Appropriate mobile and transportable
power system shall be including in disaster planning and available in a 7X24 basis.
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1.6 COMMUNITY SERVICES
In contingency planning and disaster preparation, special attention shall be given to
community issues such as schools, nursing homes, hospitals, and medical institutions. Assisted
care communications networks shall be provisioned for elderly and senior citizens along with
evacuation plans and location aware systems.
1.7 NETWORK SECURITY
Security element is a fundamental requirement for a DR/EM network. This includes content
reliability, access control and authentications. In addition, since the rescue workers may come
from various organizations with different levels of responsibility and security clearance, access
to appropriate information distributed in the network shall be carefully assigned. The network
security issue becomes more complex for a mix of heterogeneous network involved in the rescue
mission.
In general, the network security (authentication, authorization and access) can be achieved
by using standard products including data encryption, digital certificate, digital signature and key
management techniques. For a host of heterogynous networks, depending to networks
architecture and protocols, overall network security could be achieved by employing various
security platforms that are interfaced via appropriate security gateways. Security gateways shall
be located at physically secure locations with limited access. Two-way encryption products that
meet military and commercial standards are available for satellite communications. AES,
TRANSEC and FIPS levels of securities are also available for more mission critical applications.
1.8 HETEROGENEITY AND NETWORK INTEGRATION
During a disaster condition, it is usually required that several networks of different entities
to interface and exchange information. However, the network platforms and equipment
belonging to different agencies within the disaster area mainly is different, in terms of both the
protocols and equipment. Despite these differences, end-to-end communications shall be
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established between certain entities involves in the rescue operation and emergency management
including the law enforcement agencies, local hospitals, ambulance centers, emergency vehicles,
first-responders' network and communications infrastructure. Interoperability of any DR/EM
network with supporting agencies and organization is the key for DR/EM effective operation.
To achieve this interoperability the DR/EM networks of various agencies within a given region
shall be integrated using appropriate switches and gateway equipment. Joint exercise and tests
shall be routinely conducted to conform total network health status and interface functionality.
1.9 RESOURCE TRACKING AND LOCATION AWARENESS APPLICATIONS
For effective EM operation, it is critical to have the real-time tracking and positioning
information of some deployed resources including certain first responder personnel, vehicles,
ambulances, police and field equipment. Resource localization enables the EM managers to
make highly informed decisions regarding use and assignment of the field resources. Satellite
imagery and satellite navigation systems play a key role and greatly improve emergency
response effectiveness and outcomes.
A typical use of satellite imagery and asset tracking is
illustrated in Figure-8. For example, in a health emergency care scenario, the position of the
possible victims must be established to coordinate the rescue operation; or in managing a
hurricane response, it is useful to identify the disaster scenarios and set up alternative
transportation or communication solutions relating to an evacuation plan [3].
From technical point of view, localization of sensors, network nodes, and user terminals
within an disaster area is one of the key issues of EM/DR communication systems. To meet the
typical requirements of an emergency communications system, a localization protocol must be:
•
Robust to node failures
•
Insensitive to noise
•
Low error in location estimation
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•
Flexible in any terrain
Currently, two types of localization techniques address these challenges: beacon-based and
relative location-based. Both techniques can use range and angle estimations for sensor node
localization through received signal strength (RSS), time of arrival (TOA), time difference of
arrival (TDOA), and angle of arrival (AOA) [4].
1.10 SENSOR DEVICE NETWORK
Wireless sensor devices are routinely used in disaster scenarios to detect and monitor certain
critical issues including the vital signs of the rescue workers. The data generated by these
devices shall be communicated to the emergency management center (EMC) via a secure local
data network. Wimax or WiFi networks could be used to interconnect the sensor devices to the
EMC and/or satellite DR/EM network. An illustration of sensor device network is presented in
Figue-9.
1.11 TYPICAL DEMAND ASSIGNED NETWORKS
Satellite networking is reaching an inflection point, facilitated by net-centric architectural
influences, for larger coverage area and ease of deployment. The role of IP protocol is central to
efficient interoperability across diverse wired and radio transport technologies for both terrestrial
and space components. Satellite networks are extensively used for commercial and
governmental EM/DR applications and substantially extend the geographical range of distributed
information systems. However, currently deployed networks have mainly been dominated by
transponder point-to-point Single Channel per Carrier (SCPC) techniques or demand assigned
architectures that are not up to date and do not feature extensive intelligent capabilities on
network processing, resource management capabilities. This results in inefficient utilization of
the network resources, namely satellite power and bandwidth, and consequently increases
network operation cost.
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Recent developments in communications technology, signal processing and computing
techniques resulted in next generation intelligent satellite networks with much greater resource
utilization efficiency, These network can detect and adapt to network operation environment and
traffic conditions, and manage the network resources accordingly in a much effective and smart
manner according to certain predefined protocols and criteria. Unfortunately, most of the
existing networks utilize a legacy technology and a demand assigned criteria that assigns the
resources in a first come first serve basis. Network bandwidth is assigned in a mechanistic
fashion, with minimal intelligence and dynamism involved.
In many cases, the network architecture is based on SCPC technology that requires a
permanent allocated bandwidth no matter the link is utilized or not. Even some platforms with
TDMA access technology are not equipped with sufficient resource management techniques.
Although TDMA and SCPC-DAMA technologies were great improvement over the traditional
fixed SCPC technology, still these have great limitations in terms of network resource utilization
efficiency.
To illustrate the inefficiency of the legacy networks, a typical SCPC-DAMA network is
presented and explained as shown in Figure-10 and Figure-11. For simplicity, in this example,
network contains four fixed rate channels; two 1.544 Kbps channels, one 768 Kbps channel and
one 512 Kbps channel.
Depending on the network sites requirement and traffic load, these channels are assigned to
connect any two given points based on a simple connection criteria that "who requested first".
The allocated channel will remain as assigned, i.e. "channel B" connecting the EM/DR center
and remote site-3 until one of the sites terminates the connectivity. Then, this channel (Channel
B) becomes available and can be assigned to another connectivity within the network.
As mentioned, one of the key issues in the EM/DR networks design is the network cost
containment. Many organizations and agencies recognize the necessity for a EM/DR network
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but either they cannot do it due to budget restrictions or they deploy a partial ineffective network
that practically would be a minimal help during a disaster condition. The legacy networks are
expensive due to inefficient utilization of satellite capacity. Ku-Band satellite bandwidth, which
is mostly used for EM/DR networks applications, averages at $5000 per MHz per month. In
order to stay prepared, an organization shall pay this recurring cost every month plus the network
operation and maintenance fees. This demands a much more cost effective and dynamic solution
that in one hand supports challenging dynamic requirements of an EM/DR network and on the
other hand reduces the network operation cost to an affordable level.
2.0 INTELLIGENT NETWORKS
In order to address the cost containment and inefficiency issues of the EM/DR networks, this
project examines next generation intelligent platforms with advanced technologies and network
architecture with emphasis on the following solutions:
•
Cloud Communications satellite networks
•
Service-Oriented Architecture (cloud engineering)
•
Advanced dynamic access methods
•
Advanced QoS utilization
•
Efficient transmission techniques
•
Adaptive network performance and Radio Resource Management systems
Recently, satellite network technology providers have recognize the need for more
intelligent platforms and have developed certain features that coupled with an appropriate
network design can improve network cost and performance considerably. However, there is still
need for greater evolutions. Next generation satellite networks with drastically improved costperformance characteristics could be developed based on the concepts described in following
sections.
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2.1.0 CLOUD COMMUNICATIONS
The National Institute of Standards and Technology (NIST) provides a concise and specific
definition for cloud computing: "Cloud computing is a model for enabling convenient, ondemand network access to a shared pool of configurable computing resources (e.g., networks,
servers, storage, applications, and services) that can be rapidly provisioned and released with
minimal management effort or service provider interaction." In telecommunications, a "cloud" is
the unpredictable part of any network through which data passes between two end points of the
network.
The Internet and ubiquitous broadband created a revolution in the way enterprises manage
applications giving rise to the concept of Cloud Computing. A similar transformation is now
changing the way enterprises can communicate giving birth to Cloud Communications.
The concepts of cloud communications conceives from the cloud computing that is based on
resource sharing and ubiquitous communications environment that results in cost containment,
productivity improvement and better network performance. Similar to cloud computing, cloud
communications provides network services that do not require end-user knowledge of the
physical location and configuration of the system (in this case "satellite") that delivers the
services. Intelligent network management system (NMS) and Radio Resource Management
(RRM) system would control the resource allocations and establishes requested connectivity.
Parallels to this concept can be drawn with the electricity grid, where end-users do not know the
origin of the power source.
Cloud communications is clearly gaining momentum in the information technology and
telecommunications world as enterprises look for more efficient ways of distributing and
disseminating information. This will dramatically change telecommunications and information
technology landscape in upcoming years and more sophisticated satellites platforms will fuel
even more demand for cloud-computing applications.
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However, cloud technology demands what the Internet has been so effective at delivering:
seamless routing across multiple networks without regard to geography or ownership. This has
tended to leave satellites on the sidelines when it comes to accessing the cloud cost benefits and
effectiveness. Nevertheless, the recent demands for communications around the world specially
for areas without substantial terrestrial infrastructure and latest advances in wireless technology
are opening opportunities for cloud satellite networks (I.e. 03B satellite networks and services).
Further, this is facilitated by advancement and convergence of many enabling technologies and
concepts by combining the operational benefits of virtualization, scalability along with advanced
access techniques, routing schemes and system design benefits of service oriented architecture
(SOA).
Utilizing combination of sophisticated routing techniques, access methods and intelligent
network management systems, satellite cloud becomes an attractive solution for situations that
terrestrial infrastructures does not exist or substantially damaged because of disaster. Dr,
Hamadoun Toure, the Secretary General of International Telecommunications Union (ITU)
recognizes the effectiveness of ubiquitous satellite network as:
"Owing to their ease of deployment, wide-area coverage, and independence from the local
telecommunications infrastructure (which may be lost during a disaster), mobile satellite
terminals and ancillary equipment are very effective means of providing emergency
telecommunication services for relief operations. In order to strengthen disaster preparedness.
Mobile Satellite Services (MSS) systems should be deployed ubiquitously, especially in disasterprone regions". [1]
2.1.1 BASIC CONCEPT
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The concept of cloud communications can be materialized by close cooperation among
agencies, organizations, satellite service providers and regulatory bodies. Considering specifics
of the EM/DR communications and functional requirement, an statewide, nationwide or even
global cloud satellite network can be implemented as the wide area EM/DR network.
For
example, at the state level, all state agencies can use a single cloud network while maintaining
their own autonomous communications and reducing the overall network cost. At the federal
level, a national EM/DR cloud network can be materialized with much greater economical
benefits.
Community cloud may be established where several organizations have similar
requirements and seek to share infrastructure so as to realize some of the benefits of cloud
communications.
For a community cloud with several tenants, the costs are spread over fewer
users than a large public cloud. However, for EM/DR applications, due to random nature of
disaster events, the economic benefit would be much greater. Community clouds usually offer a
higher level of privacy, security and/or policy compliance due to limited number of tenants.
For the EM/DR cloud network, the economical advantages are directly related to the disaster
event Correlation Coefficient among the community members. Lesser the correlation, greater
the cloud economic gain. Based on the random nature of disaster, and the fact that disaster
events are not strongly correlated for distant geographical locations, EM/DR network resources
can be economically shared without any effect on network performance and availability. For
example, it is less probable that two states that are geographically apart, (i.e. California and New
York), to have the same disaster event than New York and New Jersey that are neighboring. Of
course the geographical separation is only one factor in disaster event correlation. Other factors
shall be studied and accounted when designing an EM/DR cloud network.
At any rate, with
appropriate network architecture, there would be considerable economic benefits for the EM/DR
community members.
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Required cloud network capacity can be estimated based on tenants' traffic loading
predications and disaster event correlation factor among the tenants. Obviously, less disaster
event correlation among tenants results in a smaller satellite capacity and greater bandwidth
efficiency. A generic view of a satellite cloud network is shown in Figure-12.
2.2 CLOUD ENGINEERING
In addition to established satellite systems engineering practices, a cloud network design
shall include certain design principles specific to cloud communications.
Essentially, cloud
communications, with a multidisciplinary organization, is a service oriented network. Cloud
engineering could benefit greatly by employing a "Service-Oriented Architecture" (SOA) design
approach. Service Oriented Architecture is a flexible set of design principles used during the
phases of system development and integration in computing systems and platforms. A system
based on a SOA will package functionality as a suite of interoperable services, processes and
criteria that can be used within multiple, separate systems and entities from several business
domains.
In other words, SOA defines how to integrate widely disparate applications and service
components within a shared environment using multiple implementation platforms.
In
communications systems, for example, little development has taken place of solutions that use
truly static bindings to talk to other equipment in the network. By formally embracing a SOA
approach, such systems can position themselves to stress the importance of well-defined, highly
interoperable interfaces [5].
Clearly, system agility, scalability, data security and network
elasticity shall be a great concern of cloud engineering.
In a multitenant cloud environment, the overall network security and tenants' data isolation
are critical components of cloud engineering.
Basically, network security shall address data
isolation, content encryption, authentication and access control.
Secure partitioning schemes
including Network Virtualization (NV), Partitioning Communication Systems (PCS), W-LANs,
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VLANs and fire-walls shall be an integral part of a multitenant cloud network design.
Partitioning Communication System (PCS) is a high-assurance computer security architecture
based on an information flow separation policy. PCS extends the four foundational security
policies of MILS (Multiple Independent Levels of Security) to the entire network including:
•
End-to-end Information Flow
•
End-to-end Data Isolation
•
End-to-end Periods Processing
•
End-to-end Damage Limitation
The term Network Virtualization refers to the creation of logical isolated network partitions
overlaid on top of a common physical infrastructure as illustrated in Figure-13.
Each partition is logically isolated from the others, and must behave and appear as a fully
dedicated network to provide privacy, security, and an independent set of policies, service levels,
and even routing decisions. The architecture of an end-to-end network virtualization solution
can be separated in the following three logical functional areas:
•
Access control
•
Path isolation
Services edge
While each area performs several functions at the mean time must interface with the other
functional areas to provide the end-to-end solution as shown in Figure-14 [6].
The access control functional area identifies the users or devices logging into the network so
they can be successfully assigned to the corresponding groups.
An identity is an indicator of a
client in a trusted domain and it is used as a pointer to a set of rights or permissions to allow for
client differentiation. Identities not only can be used as security mechanism, but also can be used
to provide permissions to specific service within a domain.
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Although network services are
arbitrary, this represents a linkage to path isolation techniques to provide a holistic form of
differentiation between various types of clients.
Access control also promotes authentication:
the process of establishing and confirming the identity of the client requesting services.
For wireless access, the concept of a port can be replaced by the association between client
and access point (AP). When authorizing a wireless device, the association is customized to
reflect the policy for the user or device. This customization can take the form of the selection of
a different wireless LAN (WLAN), VLAN, or mobility group, depending on the wireless
technology employed. When an endpoint is authorized on the network, it can be associated to a
specific group that typically corresponds to a separate partition or domain. Thus, the
authorization method ultimately determines the mapping of the endpoint to an end-to-end virtual
network. The current state of the technology provides broad support for VLAN assignment as an
authorization alternative. In essence, VLANs may be mapped into separate policy domains,
which define the correct entrance criteria into the path isolation architecture alternatives.
In general, cloud operational issues including network security can be viewed within
network
homogeneity concept that
shall
be
addressed
by
systems
standardization,
interoperability, communications protocols and interfaces, and use of SOA design approached.
Basically, network standardization takes the EM/DR telecommunications to next level of
engineering resolving many major interoperability issues.
standardization has been one of the key activities of ITU.
Emergency telecommunications
The new ITU standards were
developed in accordance with resolutions adopted at the ITU Plenipotentiary Conference in
2006, the ITU Radio-communication Assembly and the World Radio-communication
Conference in 2007[7]. Recommendation ITU-R SI001-2 provides information on the range of
radio-frequencies that can be used by fixed-satellite service (FSS) systems for emergency and
disaster relief operations. Recommendation ITU-R Ml 854, provides information on the range of
radio-frequencies for Mobile-Satellite Service (MSS) in order to enable a variety of functions
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such as voice and data communication, field reporting, data collection, position information, and
image transmission. The adopted standard enhances the quality of satellite communications
during emergencies and will greatly improve the network interoperability issues. It is important
to note that wireless sensor communications, mobile and transportable systems, community
health networks, assisted care network etc. all shall be fully integrated as part of cloud EM/DR
network.
Satellite capacity resources associated with cloud network may reside on multiple satellites
operating at the same frequency range, for example Ku-band. Satellite multi-feed antennas can
be utilized to transmit and receive to/from multiple satellites. This further enhances network
flexibility in term of satellite resource availability and network efficiency.
Regarding the
network topology, cloud shall be a fully agile network supporting both mesh and star
connectives.
2.3 CHANNEL ACCESS METHODS
In telecommunications and computer networks, a channel access method or multiple access
method defined as a technique that allows several communications terminals to use the same
transmission medium to connect to remote terminals by sharing the same network capacity.
Examples of shared physical media are wireless networks, bus networks, ring networks, hub
networks and half-duplex point-to-point links.
A channel-access scheme is based on a
multiplexing method, that allows several data streams or signals to share the same
communications channel same or physical medium. A channel-access scheme is also based on a
multiple access protocol and control mechanism, also known as media access control (MAC).
This protocol deals with issues such as addressing, assigning multiplex channels to different
users, and avoiding collisions. The MAC-layer is a sub-layer in Layer 2 (Data Link Layer) of the
OS I model and a component of the Link Layer of the TCP/IP model. Four fundamental channel
access schemes can be listed as:
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•
Frequency Division Multiple Access (FDMA)
•
Time division multiple access (TDMA)
•
Code division multiple access (CDMA)
•
Space division multiple access (SDMA)
2.3.1 FREQUENCY DIVISION MULTIPLE ACCESS (FDMA)
FDMA is a frequency domain access method that is based on the frequency-division
multiplex (FDM) scheme, which provides different frequency bands to different data channels.
Basically, FDMA can be viewed as a channelization protocol assigning users an individual
allocation of one or several frequency bands.
A related technique is wave-length division
multiple access (WDMA), based on wavelength division multiplex (WDM), where different
users get different colors in fiber-optical communication. It is important to distinguish between
FDMA and frequency-division duplexing (FDD).
While FDMA allows multiple users
simultaneous access to a certain frequency spectrum, FDD refers to how the radio channel is
shared between the uplink and downlink (for instance, the traffic going back and forth between a
mobile-phone and a base-station). Furthermore, frequency-division multiplexing (FDM) should
not be confused with FDMA. The former is a physical layer technique that combines and
transmits low-bandwidth channels through a high-bandwidth channel.
FDMA, on the other
hand, is an access method in the data link layer. An example of FDMA systems were the firstgeneration cell-phone systems.
FDMA access technique is extensively used in satellite
communications.
2.3.2 TIME DIVISION MULTIPLE ACCESS (TDMA)
TDMA is a time domain channel access method for shared medium of the
telecommunications networks. It allows several users to share the same frequency channel by
assigning a transmission time slot to each user. The users transmit in rapid succession, one after
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the other, each using his own time slot. TDMA is extensively used in satellite systems, combatnet radio systems, and PON networks for upstream traffic from premises to the operator.
In
dynamic time division multiple access, a scheduling algorithm dynamically reserves a variable
number of time slots in each frame to variable bit-rate data streams, based on the traffic demand
of each data stream.
Packet based multiple-access schemes are also time domain multiplexing access methods
but more with a dynamic (random) time slot assignment rather than an static cyclically repetitive
frame structure.
The packet based multiplexing, due to its random character, may be
characterized as statistical multiplexing resulting in highly dynamic bandwidth allocation.
Followings are major characteristics of the TDMA access method:
•
Shares single carrier frequency with multiple users
•
Non-continuous transmission makes handoff simpler
•
Slots can be assigned on demand in dynamic TDMA
2.3.3 CODE DIVISION MULTIPLE ACCESS (CDMA)
CDMA is an advanced wireless access technique based on spread spectrum technology that
allows numerous communications channels (carriers) to transmit information simultaneously and
at the same frequency occupying a single transmission capacity. Spread Spectrum technique is a
method that transmits a signal by spreading its bandwidth over a broad range of frequencies. The
bandwidth for the transmitted signal is much greater than the bandwidth of the original content to
be transmitted. CDMA requires a unique identifying code for each channel (transmitter) that is
embedded in the communications signal. Choosing the code that used to modulate the signal is
crucial for performance of CDMA systems. The best performance will occur when there is good
separation between the signals from various users. The separation of the signals is made by
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correlating the received signal with the locally generated code of the specific user. If the signal
matches the desired user's code with a high correlation, the system can extract that signal. In
general, CDMA includes to two basic categories: synchronous (orthogonal codes) and
asynchronous (pseudorandom codes).
This access method, due to its advantages, extensively used in military communications
applications, satellite communications and it is the foundation for 3G wireless networks
worldwide. Regarding bandwidth utilization efficiency, CDMA has great advantages over
FDMA and TDMA.
2.3.4 SPACE DIVISION MULTIPLE ACCESS (SDMA)
SDMA is a channel access method based on creating parallel spatial pipes through spatial
multiplexing and diversity. Using smart phased array antenna systems and dynamic directive
antenna technology both at transmitters and receivers, SDMA segments space into the
transmission sectors creating a much effective communications channel and superior
performance in radio communications.
2.3.5 CLOUD NETWORK ACCESS METHOD
The above mentioned access techniques highlight the main category of access methods.
However, there are varieties of access techniques that fall under above-mentioned main
categories. Considering the dynamic and unpredictable nature of the EM/DR network and in
order to achieve an optimal level of resource utilization efficiency, a fusion of various access
techniques shall be utilized.
For cloud communications, the concept of access method shall be
contemplated in conjunction with the intelligent network concepts and characteristics. A highly
intelligent network management system (NMS) and radio resource management (RRM) system
dynamically assigns an appropriate access method that fits the real time condition of the
network. NMS and RRM system, based on the real time data collected from network parameters
and traffic pattern, shall use an adaptive access method that best optimized to that condition. In
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certain scenarios, depending to traffic pattern and QoS requirements, the network bandwidth may
automatically partitioned to some subset bandwidth and a certain access technique is applied to
each subset. An illustration of various channel access methods is shown in Fugure-15 [8]
2.4 QUALITY of SERVICE (QoS)
Quality of Service (QoS) refers to a broad range of networking technologies, techniques and
protocols that are collectively utilized to provide guarantees and predicable service level per
predefined performance specifications. In other words, the QoS is the ability to provide
different priority to different applications, users, data flows, or to guarantee a certain level of
performance to a particular data flow. This involves management and control of network
resources such as bandwidth reservation, throughput and data flow management, power and
bandwidth availability settings against the network service quality and connectivity
requirements. This resource management is done based on certain predefined protocols and
policies for various types of network services, applications and groups. For example, traffic
shaping, also known as "packet shaping," is the practice routinely used in regulating network
data transfer to assure a certain level of performance and QoS. This practice involves delaying
the flow of packets that have been designated as less important or less desired than those of
prioritized traffic streams. Regulating the flow of packets into a network is known as "bandwidth
throttling." Regulation of the flow of packets out of a network is known as "rate limiting."
An alternative to complex QoS control mechanisms is to provide high quality communication
over a best-effort network by over-provisioning the capacity so that it has sufficient resources for
the expected peak traffic load. This is basically how most legacy networks are operating now by
sizing network resources to peak requirement which is specially cost prohibited for EM/DR
applications. Further, in legacy network, certain network components may not be designed to
support prioritized traffic or guaranteed performance levels, making it much more challenging to
implement a QoS solutions across various segments of the network. QoS is especially important
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for the new generation of IP based applications such as VoIP, video streaming, IPTV, file
transfer and content distribution services while there is not an abundant network resource.
The elements of network performance within the scope of QoS often include availability
(uptime), bandwidth, throughput, latency and bit error rate. A network monitoring system must
typically be deployed as part of QoS, to insure that network is performing at the desired level and
per defined priorities and policies. QoS technology can be applied in a group level (GQoS) and
Network level (NQoS) in cloud communications model. GQoS can provide service levels based
on any specific group that can be defined in the network i.e. type of business process, site
locations, user group, IP applications and data formats. This allows for a greater number of
service level possibilities, resource management and traffic prioritization ideal for EM/DR
applications. In a cloud network model, GQoS provides a significant increase in bandwidth
management capabilities resulting in more flexibility with traffic configuration, prioritization and
consequently more cost savings over conventional QoS. Further, in the satellite networks GQoS
can be defined on the outbound and inbound traffic adding to greater resource management
capabilities. An example of a GQoS structure is presented in Figure-16.
2.5 TRANSMISSION EFICIENCY
In telecommunications systems engineering, the transmission performance quality is directly
related to the link bandwidth quantity introducing a conflicting problem. In one hand, the
network capacity cost shall be maintained in a realistic level and on the other hand, the
transmission performance shall meet a minimum acceptable specification. Digital Video
Broadcasting Satellite- Second Generation (DVB-S2) standard addresses the challenge of costeffectively transmitting of high-quality video contents and data services via satellite.
In addition, in cloud communications, substantial transmission gain can be achieved by
multiplexing the data streams of smaller carriers to a single larger carrier by time division
multiplexing—single carrier network vs. multicarrier network. This is particularly important in
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satellite communications that requires transponder input and outputs back-offs for multicarrier
operation environment to avoid intermodulation problems. The single carrier approach may not
be practical for a single agency or organization due to lesser traffic load and fractional
transponder capacity requirement, but can be easily materialized for a multitenant cloud network.
Satellite capacity efficiency achieved with single carrier transmission can exceed 3 dB (100%)
even without accounting the potential modulation and coding gains.
In general, the transmission technique of cloud network also shall be viewed within the
framework of intelligent networks allowing full agility in transmission parameter controlled by
RRM system. At the present stage of technology, DVB-S2 transmission technique is the closest
one that can satisfy cloud communications dynamic modulation, coding and transmission
attributes. The DVB standards committee formally approved the DVB-S2 standard in 2005.
Although digital video broadcasting was the main driver for DVB-S2 development, the
committee took the opportunity to incorporate an "interactive data" element in the standard that
is developed specifically for VSAT applications. This transmission technique, implemented in
satellite outbound and inbound channels results the most efficient (Bits/Hz) physical layer data
delivery solution available today. The main achievements and performance benefits of DVB-S2
standard results from development and implementation of three core techniques which form the
heart of DVB-S2 standard:
1 - An highly efficient forward error correction (FEC) coding
2- Optimized modulation schemes
3- Adaptive coding and modulation (ACM) technology
As noted, one of the great advancements within DVB-S2 is a new highly powerful FEC
coding that is a key factor in achieving excellent performance in the presence of high levels of
noise and interferences. It is currently anticipated that the efficiency of this EFC is so near the
optimum that it is unlikely that there will be a need for further enhancements. DVB-S2 FEC
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scheme is based on concatenation of BCH (Bose-Chaudhuri-Hocquengham) code along with
LDPC (Low Density Parity Check) inner coding which has been shown to provide a 30 percent
increase over the DVB-S standard and a 15 percent improvement over Turbo FEC coding.
A second major enhancement in the DVB-S2 standard is the provision of higher order
modulation schemes. Four modulation schemes include QPSK and 8PSK which are intended for
non-linear applications where satellite transponder driven close to saturation point, and newly
introduced 16APSK and 32APSK modulations, which requiring a higher level of C/N and are
mainly targeted for professional applications such as news gathering and interactive services
with larger satellite antennas. Contrary to previous standards, DVB-S2 recognizes the reality of
non-linear characteristic of the satellite channels and defines new constellations that are
optimized for non-linear environment as shown in Figure-17. DVB-S2 defined FEC rates and
modulation schemes are shown in Table-1.
However, the most significant achievement of DVB-S2 is the provision of Adaptive Coding
and Modulation (ACM) implemented within the standard. Using the return-channel of the
network, real-time feedback on received data and transmission performance parameters can be
forwarded from the remote terminals to NMS. With DVB-S2/ACM, the carrier to each remote
terminal can be operated at the most efficient coding and modulation combination possible for
that terminal at any given time. ACM enables each remote terminal to operate at its most
efficient coding and modulation scheme, at any moment in time, depending on location within
the satellite contour, antenna size, and atmospheric conditions.
The management of ACM functions is performed by an ACM router and router manager.
The combinations of these two devices receive the quality of reception data from the remote
receivers and instruct the modulator which modulation and FEC to use whilst managing the data
rate flow into the modulator. With this, there would be no need any more to design a network
for the worst-case terminal basis. ACM automatically monitors the condition of the data link to
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each remote terminal and adjusts the modulation and coding scheme for the outbound carrier
continually in real time basis. This capability further can increase network resource utilization
efficiency, improve network performance and availability.
2.5.1 DVB-S2 PERFORMANCE
As noted, DVB-S2 performance is extremely close to the theoretical performance of
Shannon limit. Taking advantage of powerful FEC coding, ACM technique and optimized
modulation schemes it delivers significantly higher throughput in a given satellite transponder
bandwidth than the earlier standards. DVB-S2 throughput for a typical 36 MHz transponder in
terms of some selected modulation and coding schemes ranges from 43 Mbps to 77 Mbps as
presented in Table-2. It is important to note that a typical full transponder throughput for SCPC
transmission would be around 42 Mbps. In general, DVB-S2 transmission gain ranges from 30%
for broadcast operations and up to 100% for interactive data applications. Satellite capacity for
interactive and point-to-point applications may be improved by 100%-200% using combination
of ACM functionality with the use of a return channels to achieve a closed-loop adaptive coding
and modulation functionality [9]. DVB-S2 performance in terms on required C/N versus
spectrum efficiency in the Additive white Gaussian Noise (A WGN) channel is presented in Table3.
2.6 RADIO RESOURCE MANAGEMENT SYSTEM
Radio Resource Management (RRM) system shall be an integral part cloud network
effectively controlling network resource allocation based on real-time network performance and
operation parameters collected from the network. From the functional point of view, RRM
system interacts with network management system (NMS), protocol processor (PP) and other
intelligent components of the network (for example ACM system) to effectively manage network
resources. This may involve strategies and algorithms for controlling network parameters such
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as transmit power, channel allocation, C/N, channel bandwidth, channel QoS, data rates,
handover criteria, modulation scheme, error correction coding scheme, etc.
The end objective of RRM system is to utilize the limited radio spectrum resources as
efficiently as possible without sacrificing any significant performance. In general, within the
frame work of intelligent networks, the smart component of the cloud network including RRM
system, NMS, PP, ACM, etc. shall be based on the SOA and a systemic design approach using a
distributed computing architecture.
RRM is especially important in systems limited by co-channel interference such as cellular
systems and satellite networks that homogeneously covering large areas, and wireless networks
consisting of many adjacent access points that may reuse the same channel frequencies. RRM
technology is widely used in cellular network and can be easily adapted to satellite cloud
communications environment.
In fact, DVB-S2 standard employs this concept by use of
adaptive coding and modulation (ACM) system described above.
2.7 STATE OF INDUSTRY
Although recently there were great technological advancements in the areas of intelligent
satellite networks and platforms, still the industry somehow lagging behind the market dynamics
particularly in the area of cloud networking. In order to make satellite networks more attractive
and cost-effective, further developments are needed to reduce overall networks cost. Many of
the network service providers and VSAT platform technology companies are trying to catch up
with the market demands.
On November 2010, ND SatCom, a German based satellite technology company, introduced
its cloud computing (cloud communications) solution, XWARP, in partnership with Citrix, a US
based information technology Solution Company [10]. XWARP is based on integration of
various technologies from both companies including Citrix Virtualization, a zero-latency engine,
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an intelligent management system for secure satellite network connection and SkyWAN satellite
modem.
Many satellite industry companies and customers have voiced support for the future of
satellite in the cloud computing world. Duncan McCarthy, a research scientist with the U.S.
National Geospatial Intelligence Agency (NGA), told Satellite Today that he expects satellite
players to invest more in having a cloud computing capability. '7 would suspect satellite
companies would be investing in their own cloud computing capabilities at ground stations so
data can be brought down and processed into information so more sophisticated products might
quickly be delivered to customers." CTO of GeoEye corporation (a US based earth-imaging
satellite company) Brian O'Toole said cloud computing could enable the satellite industry to
build and offer new services to new sets of customers through subscription-based business
models and cited Netflix as an example of the solution's commercial benefits. "Customers may
want to subscribe to imagery content for an area of interest and have that streamed directly into
their business environment. If you take a look at what NetFlix is doing with on-demand services,
you will see that they are starting to shift to focus on providing movies on demand through your
cable box and over the Internet. I think the next 10 years will be exciting as we see new
information products emerge with the added benefit of flexible access and delivery through cloud
enabled solutions."[10]
ND SatCom claims that "XWARP® provides greater efficiency, lower operating costs and
increased productivity with any company with a distributed network topology and VoIP, Video,
VPN, streaming media and internet access applications.
ND SatCom's XWARP® shares
bandwidth and significantly reduces satellite capacity by dynamic bandwidth allocation.
Further, an advanced QoS allows efficient bandwidth allocation and priority management while
mapping the network resources in terms of remote terminal locations.
Mesh capabilities of
XWARP® allow video, voice and data to be directly transferred to other remote stations without
260
using a headquarters (hub) connection. As a result of this single hop, infrastructure, bandwidth
usage and costs are reduced.''''
Another intelligent satellite network front-runner is iDirect, a US based VSAT platform
technology company.
This company earned credits for many innovative developments and
solutions including their shared network architecture environment that easily can be adapted to a
cloud communications model, Group Quality of Service (GQoS) protocols, and Deterministic
Time Division Multiple Access (D-TDMA), Multi Frequency Time Division Multiple Access
(MF-TDMA) techniques.
iDirect GQoS provides a comprehensive set of powerful and state-of-the-art features that
allow a significant increase in bandwidth management capabilities of the network. This is
especially important when prioritizing traffic in a shared network environment or cloud
communications - resulting in greater flexibility for traffic configuration, prioritization
consequently more bandwidth savings and improved service quality. When combining GQoS
with DVB-S2/ACM it allows network operators to increase DVB-S2 efficiency gains by
combining multiple small networks into a single, larger carrier. It also allows the network
operator to maintain distinct QoS settings by remotes, bandwidth groups and applications. By
tightly integrating ACM and GQoS, service providers can create more flexible service offerings
and improve user satisfaction in geographies commonly impacted by adverse weather conditions.
For example, they can establish Extended Information Rate (EIR) options that maintain a fixed
Committed Information Rate (CIR) during periods of rain fade, giving the end-user the choice of
more service level guarantees at different price points
Utilizing D-TDMA and MF-TDMA access methods, iDirect technology can dynamically
manage the inbound and outbound satellite capacity in a mesh or star topology environment
resulting in additional cost saving. The system constantly analyzes capacity demand at the
network level and allocates bandwidth as frequently as eight times per second resulting in a
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payload efficiency of about 98%. This architecture is ideal for enterprise networks that have
burst y TCP/IP traffic and also support real-time traffic such as VoIP, video conferencing and
mission critical applications. MF-TDMA provides fast frequency hopping on a burst-by-burst
basis and provides the highest efficiency in capacity usage and allocation. iDirect was one of the
pioneers of Turbo Product Coding (TPC), a forward error correction (FEC) scheme for the
inbound / return channel that improves link performance and reduces latency. TPC FEC is based
on an iterative decoding technique, which recycles partially decoded messages back through the
process. For the remote, this translates into a reduced need for retransmission, and allows more
efficient use of satellite bandwidth. As a follow-on iDirect will introduce a new coding scheme
to which it has exclusive rights. This will offer an improvement of up to 2dB in the Eb/No
performance over TPC and add alternative block sizes, reinforcing iDirect's position as industry
leader for inbound /return channel efficiency.
iDirect also supports Paired Carrier Multiple Access (PCMA) technique that is a satellite
signal cancelling method that increases satellite capacity usage efficiency by about 50 percent.
This is accomplished by combining the uplink and downlink transmissions into the same
bandwidth, allowing two different signals to overlap in frequency and spectrum.
On July 2010, Harbinger Capital Partners announced that it will pay Nokia Siemens
Network more than US$7 billion to build a 4G satellite network called LightSquared.
According to the company, this network will reach 92 percent of the U.S. population with
expected commercial launch in the second half of 2011. LightSquared, a combined terrestrial
and satellite broadband network will be based on Long Term Evolution (LTE) technology, the
most advanced 4G wireless standard today. LightSquared is the first to integrate LTE
technology in satellite networks.
LTE standard includes a new radio platform technology that will allow operators to achieve
even higher peak throughputs than any of the existing standards and technologies.
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Initial
deployment of LTE is targeted for 2011 with an overall objective to provide an extremely high
performance radio-access technology that offers full vehicular speed mobility and that can
readily coexist with HSPA and earlier networks standards. LTE assumes a full Internet protocol
(IP) network architecture and is designed to support voice in the packet domain. Employing
state of the art radio techniques and advanced access methods, it achieves performance levels
beyond what will be practical with CDMA approaches, particularly in larger channel
bandwidths.
The latest news came on November 2010 that 03B Satellite Networks has raised$1.2 billion
to launch its first 8 Satellites designed for high speed efficient and fiber quality broadband
satellite services. [11] 03B's investors include SES (a Luxembourg based satellite company),
Google, HSBC, Liberty Global and some other world renowned investment banking companies.
The project will be carried out by Thales Alenia space, Ariane Space and Viasat, they key
companies in satellite technology area.
03B network is based on low orbiting satellites
technology with 8 spacecrafts constellation orbiting from 8,000 kilometers from the earth, four
times closer to earth than regular geostationary satellites. This technology with steerable beams,
dynamic bandwidth allocation, low propagation latency, fast configurable payloads provides
opportunity for deployments of next generation intelligent satellite networks independent of
traffic applications.
In the foreseeable future new platform and techniques will be used in satellite technology
which will extend and improve the possibilities of satellite communications beyond present
boundaries. The future telecommunication satellites will be evolved from transponded
transmission in to Information processing spacecrafts offering a comprehensive host of high
quality services with much greater network efficiency and contained operation cost.
3.0 BUSINESS MODELS COMPARISON
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In order to examine cost advantages of an intelligent cloud satellite network compared with
a legacy satellite networks we consider two scenarios with identical requirements and
performance specifications as described below. Satellite capacity is further emphasized in this
analysis based on the fact that it constitutes the main part of the network cost.
3.1 LEGACY NETWORK MODEL
Assume there are four independent networks with sufficient geographical separation and
with no strong disaster event correlation coefficient. Each network which comprise of 15
satellite terminals with 1.2 meter antenna size shall support fifteen 1500X1000 Kbps outbound
and inbound channels simultaneously at the time of a disaster (maximum network capacity).
The hub earth station located at the EMC includes a 4.5 meter antenna utilizes SCPC DAMA
technology to provide mesh/star connectivity among terminals. All four network are using
typical QPSK modulation with 3/4 FEC Turbo coding for outbound links and QPSK with 7/8
Turbo coding for return links.
The modulation and coding parameters are selected based on network performance and cost
tradeoffs. The network availability requirement is typical 99.5% and all networks use the same
North America Ku-Band satellite. For the purpose of calculation and modeling it is assumed that
these are all state-wide EM/DR networks with network hubs are located at New York, Chicago,
Los Angeles and Dallas and basically are designed for statewide emergency applications.
Therefore, satellite terminals of each network will be deployed within the given state. A simple
illustration of the legacy model networks is presented in Figure-18. To determine satellite
capacity requirements, link analysis were performed for all four networks. Summary of legacy
network specifications and attributes are presented in Table-4.
3.2 CLOUD NETWORK MODEL
To construct the cloud network model, four independent networks of the legacy model with
the same requirements, specifications and disaster event correlation is assumed to be the four
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tenants of the cloud network. Therefore, the cloud network model will comprised of four
securely partitioned networks each supporting 15 terminals with 1500X1000 Kbps outbound and
inbound channel capacity and 99.5% network availability as shown in Figure-19.
Cloud network model with mesh/star topology is based on intelligent network architecture
that dynamically adapts to network environment, traffic pattern and constantly self-optimizes by
changing network parameters. Therefore, main network parameters such as modulation scheme,
coding, QoS and access method are dynamically transforming based on overall network
condition.
To determine required cloud network capacity (shared among all four tenants) it is assumed
that there will be no simultaneous disaster event for cloud members and disaster may occur only
for one tenant at any given time. In addition, it is allowed that each tenant can use its network
routinely at 20% capacity at any given time during non-disaster periods. Naturally, this
assumption increases the resource utilization efficiency, nevertheless, even with a strong disaster
event correlation, the recommended intelligent cloud network results in greatly improved
network resource utilization. Based on above assumptions, cloud network traffic capacity is
determined as presented in Table-5.
Comparing "total network capacity" for both models in Table-4 and Table 5, it can be seen
that cloud network gain, even without accounting for other intelligent network features, reduces
the total network capacity from 150 Mbps to 60 Mbps by a factor of 2.5. Obviously this directly
translates to the cost containment on satellite capacity.
Further efficiency and performance improvements can be achieved by using DVB-S2
transmission technique, network adaptability, dynamic access methods and QOS. All four
tenants' outbound traffic is multiplexed in one 36 Mbps DVB-S2 data stream. The multiplexed
outbound data stream is received by all terminals and the traffic of each terminal, if any, is
extracted from the data stream. To determine network outbound satellite capacity, link
265
parameters calculations and link budget analysis were performed for DVB-S2. Although the
modulation and coding schemes of cloud network are constantly changing in terms of network
condition, for the calculation purposes, 8PSK modulation with 3/4 FEC coding is used.
The return channels utilize a combination of various access methods including dynamic
TDMA transmission mode. However, for calculation purposes, 8PSK modulation along with 4/5
EFC coding scheme is used. The total inbound traffic of 24 Mbps is supported by 12 carriers
each with 2 Mbps channel capacity. Each 2 Mbps carrier can be shared among all cloud
terminals by the mean of frequency hopping. Summary of cloud network link analysis is
provided in Table-6. As shown in Table-6, an intelligent cloud network can support identical
traffic load utilizing one 36 MHz transponder.
4.0 COST ANALYSIS
As noted, the main cost component for the satellite networks is associated with the satellite
capacity. As presented above, for a given traffic load, the legacy network utilizes 108 MHz of
satellite capacity while an intelligent cloud network uses only 36 MHz to support the same traffic
load. Assuming that other network operation cost factors are similar for both networks, satellite
capacity remains as the main cost factor. Ku-band satellite capacity (bandwidth) pricing varies
around $4000 per MHz and around $145000 per a 36 MHz transponder. This translates to
$432,000 per month for the legacy network versus $144,000 per month for the cloud network. In
addition the cost containment, cloud network provides a better network performance in terms of
network availability and bit error rate.
5.0 CONCLUSION
As discussed in this document, satellite networks are the primary choice for EM/DR
communications. Nevertheless, the satellite resources (power and bandwidth) are expensive and
from economical point of view, making it difficult for deploy an EM/DR satellite network. To
address this issue and to achieve significant cost containment, advanced techniques and
266
technologies are discussed in this document. As shown in Table-7, a monthly recurring cost can
be reduced with the factor of about 1/3 by utilizing multitenant intelligent cloud network.
267
REFERENCES
1 ] International Telecommunications Union, press release for new "Emergency Satellite
Communications Standards", March 2010
2] Satellite Solutions for Emergency Relief and Disaster Recovery Management, iDirect
Technologies, May 2009
3] F. Chiti, R. Fantacci, et al. Broadband Wireless Communications Systems for Emergency
Management, Proceedings of the 6th International Wireless Communications and Mobile
Computing Conference
4] C. Rohring, M. Muller, Localization of sensor nodes in wireless sensor networks, IEEE
Vehicular Technology, April 2009
5] N. Bieberstein et al., Service-Oriented Architecture (SOA) Compass: Business Value,
Planning, and Enterprise Roadmap, IBM Press books, 2005, 978-0131870024
6] Network Virtualization—Path Isolation Design Guide, Cisco Systems February 2009
7] T. Tjelta et al.. ITU-R World Radio-communication Conference 2007
8] M. Mikuszewski, K. Etykiety Universal Mobile Telecommunications System, November
2009
9] Yan Mostovoy, Maximizing Satellite Transmission Efficiency with DVB-S2, Harmonic
Corporation
10] Satellite Today Insider, November 2010
11] G. M. Lamb, The Christian Science Monitor, September 2008
268
FIGURES
^f
Satellite
Earth Station
1
Figure-1, A simple satellite link
Figure-2, Typical North America Ku-band satellite footprint
269
Terrestrial
Network
X
Satellite
Modem
TCP/IP
Router
Audio
Video
Voice
Applications
Fixed
Transportable
Mobile
(--"
LAN
y^x
Figure-3, Typical earth station
270
10"
10'
LU10
CD
10'
BPSK/QF
8-PSK
16-PSK
10'
8
10
tb/N0(dB)
Figure-4, Eb/No Values for various BER performances
271
CAMMS• (Command Anywhere Media Management System)
Figure-5, Typical mobile unit
Figure-6, Typical transportable /Flyaway unit with carrying case
US Marines set up a CommsPack
to provide interoperable radio communications (Microsoft case study)
272
US Marines set up a CommsPack
to provide interoperable radio communications (Microsoft case study)
Figure-7, A typical backpack satellite terminal
: «l
.,,,„•..,...,.,,,.' *.' elK-WU'
ll«« T;«f««UMI &» ******* «**C« CC •
With satellite map views, visualize 'off-road' locations such as parking lots,
construction sites, and identify details like the front or back of buildings for precise
asset location.
Figure-8, A typical use of satellite imagery and asset tracking data
273
Figure-9, DR/EM Wireless sensor device network illustration
.'
Control
Channel
/
/
Remote
Site -1
Network
Management
System
//
Remote
Site -3
RF
^
EM/DR
Center
Fixed Antenna
/
/
RF
Remote
Site -3
Fixed Antenna
Figure-10, Typical legacy demand assigned network
274
Channel
Channel
Channel
Channel
EM/DR
Center
.
A
B
C
0
1544 KBPS
1544 KBPS
768 KBPS
512 KBPS
Channel A
Channel C
Remote
Site-1
Channel A
Channel C
x
Remote
Site-1
unannei a
Remote
Site-2
Remote
Site-2
Channel B
Channel D X
Remote
Site-4
Remote
Site-4
Remote
Site-3
Remote
Site-3
Channel D
Scenario-2 network connectivity
Sccnario-1 network connectivity
Figure-11, Legacy demand assigned network connectivity scheme
275
Figure-12, Cloud communications concept
"p-it-pir
Figure-13, Network virtualization illustration (Cisco Networks)
276
Functions
Access Control
Path Isolation
Services Edge
Branch- Campus
WAN - MAN - Campus
Data Center- Internet Edge Campus
Authenticate client (user,
device, app) attempting to
gain network access
Authorize client into a
Partition (VLAN, ACL)
Deny access to
unauthorized clients
1.1
Maintain traffic partitioned over
Layer 3 infrastructure
Provide access to services:
Shared
Dedicated
Transport traffic over isolated
Layer 3 partitions
Apply policy per partition
Map Layer 3 Isolated Path to VLANs
in Access and Services Edge
Isolated application environments
if necessary
Network Virtualization-Access Control - Cisco
Figure-14, Network virtualization functional areas (Cisco Networks)
277
Power
Power
Power
•'
''
User 3
User 2
r
Frequency
Frequency
CDMA
TDMA
Time
Power
'l
Power
•
^Time
(1
11
n
0
12
13
.
Frequency
Frequency
FDMA/CDMA
FDMA/TDMA
Figure-15, Access Methods Illustration
278
Usef1
Outbound or
Inroute Group
BW Group 2
Service Group
Service Group
2
AppGroup 1.1
Default
Profil*
Remote 1
AppGroup 1.2
I
I
Default
Special
Profile
Remote 2
Service Group
3
APPGroupl.3
Service Group
Service Group
n
APPGroup 2.n
AppGroup 2.1
Default
Profile
Special
Profile
Remote 3
Remote ABC
iDirect Group QoS model structure
Figure-16, GQoS model structure illustration
279
Remote XYZ
Default
Profile
16AP3<
32AP9<
Figure-17, DVB-S2 Modulation Constellations
280
/
h-j^
^v..^y
45'
Network-2
Network-1
.*_,,
N.
K^
^•>
Network-3
Network-4
•
Figure-18, Legacy network model illustration
Figure-19, Cloud network model illustration
281
TABLES
FEC
8PSK
QPSK
16AP9K
32APSK
1/4
•
X
X
X
1/3
/
A
X
X
2/5
/
X
X
X
1/2
/
X
X
X
3/5
•
/
X
X
2/3
/
/
/
X
3/4
/
/
/
/
4/5
•
X
/
/
5/6
/
/
/
/
8/9
•
•
/
•
9/10
/
•
/
•
Table-1, DVB-S2 FEC Rates
Modulation
Coding
Es/No
Info
Bit/Sym
dB*
Rate (Kbps)
No Pilots
QPSK
3/4
4.30
43000
1.487472
QPSK
5/6
5.50
48000
1.654662
QPSK
8/9
6.60
51000
1.766451
8PSK
3/4
8.40
64000
2.228122
8PSK
5/6
9.70
72000
2.47856
8PSK
8/9
11.10
77000
2.646012
Table-2, DVB-S2 Throughput for 36 MHz Transponder
282
A Morello and V. Mignone, EBU TECHNICAL REVIEW - October 2004
Table-3, DVB-S2 Spectrum efficiency versus required C/N on AWGN channel
283
Network Specifications
Notes
Number of networks
4
Network Technology
SCPC DAMA
Network topology
Star-mesh
Satellite
SES-1 @ 101 Deg, West
Links per network
15
Simultaneously, 15 terminals
Hub antenna size
4.5 meter
One hub Per network
Remote terminal antenna size
1.2 meter
Network availability
99.5%
Link Bit Error Rate (BER)
10E-7
Outbound data rate/channel
1500 Kbps
Outbound capacity per network
22.5 Mbps
Inbound data rate/channel
1000 Kbps
Inbound capacity per network
15 Mbps
Total traffic per network
37.5 Mbps
Design capacity per network
Total network capacity
150 Mbps
For all 4 networks
Total outbound traffic
90 Mbps
All 4 networks
Total Inbound traffic
60 Mbps
Outbound link modulation
QPSK
Inbound link modulation
8PSK
Outbound EFC coding Turbo
7/8 @ Eb/No = 4.60 dB
Inbound FEC coding Turbo
7/8 @ Eb/No = 7.20 dB
Bandwidth spacing factor
1.35
Outbound channel bandwidth
1200 KHz
Rounded per industry practices
Inbound channel bandwidth
600KHz
Rounded per industry practices
Total required BW per network
27 MHz
15 links® (1500X1000) Kbps
Total required BW
108 MHz
For all 4 networks
No disaster event correlation
Table-4, Legacy networks specifications summary
284
Cloud network traffic specifications
Number of tenants (n)
Total outbound channels
Outbound channel data rate
Outbound traffic per tenant
Total outbound traffic, 4 tenants
Cloud network gain
Total outbound traffic, disastered tenant
Outbound traffic, per non-disastered tenant
Outbound traffic for all non-disastered tenant
Cloud network disaster time outbound
4
60
1500 Kbps
22.5 Mbps
90 Mbps
75%
22.50
4.50 Mbps
13.50
36.00
Total inbound channels
Inbound channels data rate
Inbound traffic per tenant
Total inbound traffic, 4 tenants
Cloud network gain
Total inbound traffic, disastered tenant
Inbound traffic, per non-disastered tenant
Inbound traffic fir all non-disastered tenants
Cloud network disaster time inbound
Total Cloud network capacity
60
1000 Kbps
15 Mbps
60 Mbps
75%
15 Mbps
3.00 Mbps
9.00 Mbps
24.00
60.00
Notes
With no disaster event correlation
15 per tenant
Before cloud network gain
= (n-l)/n, n= number of tenants
Only one tenant at any given time
@ 20% network capacity
Max 3 tenants
Maximum outbound traffic 4
15 per network
Before cloud network gain
= (n-l)/n, n= number of tenants
Only one tenant at any given time
@ 20% disaster time sharing factor
Max 3 tenants
Maximum inbound traffic 4
For 4 tenants
Table-5, Cloud network capacity estimate
285
Network Specifications
Notes
Number of networks
1 cloud network
Network Technology
Intelligent cloud network
Number of tenants
4
Network topology
Star-mesh
Satellite
SES-1 @ 101 Deg, West
Full duplex links per tenant
15
Simultaneously, 15 terminals
Hub antenna size
4.5 meter
One hub Per tenant
Remote terminal antenna size
1.2 meter
Network availability
99.5%
Minimum link Bit Error Rate (BER)
10E-7
Transponder operation mode
Multicarrier
Transponder input backoff
7dB
Transponder output backoff
4dB
Outbound data rate/channel
1500 Kbps
Outbound maximum traffic
36 Mbps
Outbound transmission technology
DVB-S2
With secure partitioning
Inbound and outbound links
Outbound link modulation
8PSK
dynamically adapting to network condition
Outbound link coding
V,
dynamically adapting to network condition
Required outbound Eb/No
5.02 dB
Required outbound satellite capacity
22.097 MHz
Inbound data rate/channel
1000 Kbps
Inbound maximum traffic
24 Mbps
Inbound transmission technology
Dynamic TDMA
Number of inbound links
12
Inbound link capacity
2 Mbps
Inbound link modulation
8PSK
Inbound link coding
4/5
Required inbound Eb/No
7.9 dB
Satellite capacity per link
1135 KHz
Required inbound satellite capacity
13.62 MHz
Total cloud network capacity
35.75 MHz
12 links @ 2 Mbps per link
Both for inbound and outbound
Table-6, Link Analysis Summary
286
Network Attributes
Legacy
Cloud
Number of network
4
1
Number of hubs
4
4
Number of terminals per network
15
60
Total Satellite capacity (MHz)
108
36
4000
4000
Satellite capacity pricing per MHz ($)
Estimated Capital Cost Summary
Platforms
$400,000
$600,000
$90,000
$120,000
$100,000
$100,000
Systems implementation
$80,000
$120,000
Remote terminals installation
$30,000
$30,000
$700,000
$970,000
$432,000
$144,000
Hardware maintenance
$10,000
$12,000
Software maintenance
$5,000
$7,500
Overall operation (without staffing)
$4,000
$6,000
$451,000
$169,500
Satellite terminals
Civil work
TOTAL:
Estimated Monthly Recurring Cost Summary
Transponder capacity
TOTAL:
Table-7, Networks cost summary
287
CHAPTER 10
Project 09 13 F: Bridging the Gaps Between Public Health, the Health Care System, and
First Responders
288
DEVELOPMENT OF THE UNIVERSITY CENTER FOR
DISASTER PREPAREDNESS AND EMERGENCY RESPONSE (UCDPER)
Bridging the Gaps Between Public Health, the Health Care System, and First Responders
Project 09 13 F
Final Report
Investigators: George T. DiFerdinando, Jr., MD, MPH, FACP
NJ Center for Public Health Preparedness at UMDNJ-SPH
683 Hoes Lane West, Room 129 First Floor, Piscataway, NJ 08854
Email: diferdgefgiumdnj.edu
289
Abstract
This project involved developing, conducting, and assessing education and training sessions
pertaining to all-hazards health threats resulting in public health emergencies with the potential
for mass casualties. Training sessions were in the table top exercise (TTX) model, targeting
diverse multidisciplinary elements of emergency and disaster response in counties. Training and
evaluation were performed in conjunction with federal, state, county, and local partners.
Three TTX were held, in different locations, on different topics, with varying participants: a
hydrogen fluoride spill TTX in business, health care, and emergency response units in
Gloucester County, NJ; a point of distribution (POD) Receipt, Stage, and Storage (RSS)
warehouse operation TTX with county agencies health and first responders, and educational
organizations, in Camden County NJ; and a mutual aid (MA)TTX with the Urban Area Security
Initiative (UASI) group in the four county Capital District of New York State.
290
Foreword
This project was performed by George T. DiFerdinando, MD and was sponsored by the
University Center for Disaster Preparedness and Emergency Response (UCDPER) - A
Collaborative Initiative of Rutgers, The State University of New Jersey, UMDNJ-Robert Wood
Johnson Medical School, and Robert Wood Johnson University Hospital - with support from
Department of Defense Grant No. W9132T-10-1-0001.
The views, opinions, positions, conclusions, or strategies in this work are those of the
authors and do not necessarily reflect the views, opinions, positions, conclusions, strategies, or
official policy or position of the Department of Defense or any agency of the U.S. government
and no official endorsement should be inferred.
291
Table of Contents
INTRODUCTION
293
Background
294
Objective
294
Approach
294
Scope
297
Mode of Technology Transfer
297
SUMMARY, CONLCUSIONS AND RECOMMENDATIONS
297
REFERENCES
299
Addenda
300
292
INTRODUCTION
Background
Prior to 9/11, civilian emergency response was often seen as the domain of emergency
response professionals - those school in hazard and vulnerability analysis, emergency response
and continuity of operations plans, and incident command system-based response to all hazards.
However, the scope of both 9/11 and subsequent intentional (anthrax exposures, 2001) and
'natural' emergencies (Hurricane Katrina) continue to highlight a broader group of civilian,
business, and, in some cases, military responders.
Thus, the scope of this project was to identify specific response needs within specific
emergency scenarios and, with active participation of the broader responder group, create and
then use exercise materials in the table top format, to improve readiness.
For the US Army, the impact of the problem is difficult to quantify, but real. Given the
potential for Army involvement in catastrophic emergencies on US soil, as demonstrated by
Hurricane Katrina, there exists some likelihood, however small, that the Army will need to work
side-by-side during civilian response. However, it is likely that any event that brings US Army
involvement on US soil will also be one that would have overwhelmed civilian and business
response, making side-by-side response less of a reasonable scenario.
More valuable, perhaps, is the insight that review of such exercises might bring to how nonmilitary parts of any society might work together in responding. Such insights might prove
valuable both on US soil and during deployment abroad.
This project was led by the professional and support staff of the New Jersey Center for Public
Health Preparedness at the University of Medicine and Dentistry of New Jersey (UMDNJ). Both
the prior and current Center Directors -Glenn Paulson, Ph.D., and George DiFerdinando, MD,
MPH, respectively - have direct experience responding to civilian emergencies during their
293
tenures with New Jersey state government. Dr Paulson served as Assistant Commissioner at the
NJ Department of Environmental Protection during the response to the Three Mile Island
emergency in 1979; Dr. DiFerdinando was Acting Commissioner of the NJ Department of
Health and Senior Services and thus led the public health response to 9/11 and the anthrax
exposures in NJ, in 2001. Other key staff at the NJCPHP have had formal training in both health
education and in developing, holding, and analyzing table top exercises (TTX).
Targeted users of this report and its associated work materials would be others charged with
training civilian and business respondents through the use of TTX, either keyed to the content of
the TTX developed or using these efforts as examples in which to model other content-based
TTX.
Objective
The object of this activity was to identify areas of training need in the civilian, governmental,
and business communities pertaining to all-hazards health threats that can result in public health
emergencies with the potential for mass casualties; and developing, conducting, and assessing
education and training sessions provided to diverse multidisciplinary elements of the emergency
and disaster response personnel in counties. With the TTX developed from this activity, the
knowledge, skills and attitudes of those trained would be enhanced, with the concrete potential of
improving response under real emergency conditions, both with the hazard involved as well as in
other hazard responses.
Approach
The pedagogical model of this project was twofold. Table top exercises are classified as
discussion based exercises according to HSEEP. Discussion based exercises are less structured
than a full scale exercise or drill and its primary benefit is to bring parties together to discuss and
test a plan. All three exercises that were completed were tabletop exercises. TTX can be used as
stepping stones towards a full scale or "live action" exercise in the future. Structurally, the table
294
top exercise model was chosen for its documented ability to bring out both the strengths and
weakness of those involved, during the exercise itself. The second, and in our opinion major,
benefit of the TTX model is what occurs between and among participants during the preparation
of the exercise, and the evaluation and after-action phases. 'Before and after' the TTX,
participants work to clarify goals of the TTX, in light of known needs of competency/capability
development within their respective organizations. The choice of the subject of the TTX, itself,
requires knowledge of valid hazards and/or capability needs within the community; discussion
and debate around the topic of the TTX typically initiates the process of team building among
those representatives of the community to be exercised, long before the day of the formal TTX.
Similarly, the evaluation and after action process, ideally, allows the team members - now
better known to each other - to review what happened, what it means, and what improved
individual competencies, agency capabilities, and/or community resources will be needed to
potentially improve the response exercised in the TTX, if it were to happen in real time.
For this group of exercises, we began with the insight and perceived need, explicit in the project
title, that there are gaps between health care providers, first responders, and the public health
community. Public health preparedness and emergency response is not the same as emergency
management, in general. Based on the recently developed competencies for mid-level public
health workers, there are many expectations of public health response that are distinct from
traditional emergency management.
Participant identification in the project was done by first considering which groups we
(NJCPHP) was targeting - public health, health care, first responders, etc. - and then direct
outreach, by phone or in-person. Prior contacts of NJCPHP leadership and staff were called. The
Gloucester Hydrogen Fluoride (HF ) exercise ( Addenda 01-08) developed from Dr. Paulson's
knowledge of NJ chemical production facilities, and his outreach to members of that region on
their perceived need to bring individuals together to enhance preparedness for a HF event. The
295
RSS (Addenda 09-11) and Mutual Aid (Addenda 12-15) exercises grew out of direct contacts
with members of the NJ and NY public health community by Dr. DiFerdinando. Dr.
DiFerdinando and a staff member, Ms. Rebecca Baron, attended the NJ Department of Health
and Senior Services Training and Exercise Workshop, in December 2010. All NJ local health
agencies were required to attend, as this meeting scheduled required training and exercises over
the next two year cycle. Included in the meeting were staff from numerous NJ state departments
- Health and Senior Services, Law and Public Safety, Environmental Protection, Military and
Veterans Affairs, Community Affairs, and Transportation - along with the offices of Homeland
Security and of Emergency Management. It is at this meeting that plans for the Camden TTX
came together, along with the knowledge that both NJ and NYS UASI agencies were considering
shared services trainings.
Once participants and topics were identified, Ms. Baron, working with Drs. Paulson and
DiFerdinando, began the exercise development process. Ms. Baron has had extensive Homeland
Security Exercise and Evaluation Program (HSEEP) training, and is HSEEP certified. HSEEP is
a national standard for all exercises, and many agencies are either required or voluntarily
choose to work within the HSEEP exercise rubric of capabilities. That the exercises developed
were HSEEP compatible was a draw in and of itself for participation in the exercises developed.
The development process ranged in detail from a highly structured process for the
HFEX/Gloucester exercise to a less structured process for the RSS/Camden and MA/Capital
District UASI exercises. In the latter two, after the participants identified their needs, the great
majority of development work was performed by NJCPHP staff, including performance of the
TTXs. The HFEX/Gloucester exercised differed in the scientific subject matter mastery level
needed, as exhibited in the detailed scenario (Addenda 03-04), to the number and 'rank' of
participants, and the sheer logistics of a large group working at a business site.
296
The performance of the TTXs also varied by level of complexity. The HFEX/Gloucester
exercise was more detailed, and was managed by a professional trainer with HF background,
while the RSS and MA exercises were facilitated by Dr. DiFerdinando. The TTX exercises used
both a SITMAN (situation manual) and Power Point slides to deliver the scenario to the
participants. Attached in the addenda are the SITMANs (Addenda 01, 02, 09, 12, 13) and Power
Point presentations (Addenda 03, 04, 10, 14) for the exercises.
All exercises included a formal evaluation process, including a 'hot wash' - immediate
feedback between participants at the exercise location - and a written evaluation form to be
completed and handed in for collation. The HSEEP process includes post exercise After Action
Report (AAR) development, with sharing of that report with participants. All three AAR and
evaluations have been completed, with the HFX evaluation (Addenda 05) and AAR (Addenda
06) attached.
Scope
The value of TTXs are often very dependent on an analysis of the specific strengths and
needs of those involved in the training. Thus, it's likely that the appended TTX would be most
valuable as guides to what would be used with other organizations, in other venues, rather than
being used 'as is'.
Mode of Technology Transfer
These materials could be used by Army users in creating TTXs for their own use, on
location, based on their own analysis of local vulnerabilities. They also serve to provide Army
users with a clearer view of the state oft local and regional civilian and business capabilities.
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
Three tabletop exercises, on three different, pertinent public health preparedness topics,
were developed with participant identification, involvement in the process, and participation in
297
the exercise and evaluation/After Action Report phase. The TTX included key public health,
health care, and community responders, and ranged from an exposure focus (HFEX/Gloucester)
to a materials management focus (RSS/Camden) to a personnel management focus (MA/Albany
UASI District).
Based on feedback and AARs developed with the involved individuals and agencies, it is
clear that the use of the TTX model can effectively bring together the often 'silo-ed'
organizations that must combine to in emergency response. What is required is a topic that is of
recognizable importance to the members of the community; involvement of those members in
development of the exercise; overall, agencies feel that these exercises allow them to improve
individual employee's competencies around specific hazards and/or activities; organizational
capability to respond to those hazards and/or needs; and information to apply to current plans for
continuous quality improvement. In following with the HSEEP model, the potential exists, at the
community level, to build on these discussions based exercises into full-scale functional
exercises.
298
REFERENCES
Centers for Disease Control and Prevention and the American Association of Schools of
Public Health, Public Health Preparedness & Core Competency Model, 2010. Accessed on
August 01, 2011, at www.asph.org/userfiles/PreparednessCompetencyModelWorkforceVersion 1.0.pdf
International Association of Emergency Managers, Emergency Program Manager Knowledge, Sills, and Abilities, 2011. Accessed on August 1, 2011, at
http ://training. fema. gov/EMI Web/edu/EmergProgMgr.doc.
Department of Homeland Security, HSEEP, 2011, accessed on August 03, 2011, at
https://hseep.dhs.gov/pages/1001_HSEEP7.aspx.
POC information for copyright clearances. None declared. All material in the public
domain.
299
Addenda
Hydrogen Fluoride Exercise (HFEX)/Gloucester Materials
01 HFEX Facilitator's Situation Manual Final
02 HFEX Players' Manual Final
03 HFEX Scenario - Left Screen Presentation - Final
04 HFEX Scenario - Right Screen Presentation - Final
05 HFEX Evaluation Abstract - Final
06 HFEX After Action Report - Final
07 HFEX Steering Committee Members
08 HFEX Participant List
Receipt, Stage, and Storage (RSS)ZCamden Materials
09 RSS Facilitator's Situation Manual
10 RSS Scenario Presentation
11 RSS Participant Feedback Form
Mutual Aid (MA)ZAlbany UASI Materials
12 MA Facilitator's Situation Manual
13 MA Players' Situation Manual
14 MA Scenario Presentation
15 MA Participant Feedback Form
300
01 HFEX Facilitator's Situation Manual
Situation Manual
HFEX-Hydrogen Fluoride Exercise
Exercise Date: 05/25/2010
FACILITATOR
301
STEERING COMMITTEE
Diane Anderson
New Jersey Hospital Association
Rebecca Baron
New Jersey Center for Public Health Preparedness
Robert Brownlee
New Jersey Department of Health and Senior Services
Jack DeAngelo
Gloucester County Office of Emergency Management
George DiFerdinando
New Jersey Center for Public Health Preparedness
William Donovan
Gloucester County Prosecutors Office
Mitchell Erickson
United States Department of Homeland Security
Bryan Everingham
New Jersey State Police
Kevin Hayden
New Jersey Department of Health and Senior Services
Glenn Paulson
New Jersey Center for Public Health Preparedness
Christine Poulsen
New Jersey Center for Public Health Preparedness
Dennis Quinn
New Jersey Office of Homeland Security and Preparedness
Thomas Rafferty
New Jersey State Police
Dennis Sample
New Jersey Office of Homeland Security and Preparedness
Robert Van Fossen
New Jersey Department of Environmental Protection
Scott Woodside
Gloucester County Department of Health and Senior Services
302
PREFACE
The HFEX is sponsored by the New Jersey Center for Public Health Preparedness (NJCPHP).
This Situation Manual (SitMan) was produced with input, advice, and assistance from the HFEX
Steering Committee, which followed guidance set forth by the U.S. Department of Homeland
Security (DHS) Homeland Security Exercise and Evaluation Program (HSEEP).
The HFEX Situation Manual (SitMan) provides exercise participants with all the necessary tools
for their roles in the exercise. It is tangible evidence of Gloucester County's commitment to
ensure public safety through collaborative partnerships that will prepare it to respond to any
emergency.
The HFEX is an unclassified exercise. Control of exercise information is based on public
sensitivity regarding the nature of the exercise rather than actual exercise content. Some exercise
material is intended for the exclusive use of exercise planners, facilitators, and evaluators, but
players may view other materials that are necessary to their performance. All exercise
participants may view the SitMan.
All exercise participants should use appropriate guidelines to ensure proper control of
information within their areas of expertise and protect this material in accordance with current
jurisdictional directives. Public release of exercise materials to third parties is at the discretion of
the DHS and the HFEX Steering Committee.
303
HANDLING INSTRUCTIONS
The title of this document is the HFEX- Hydrogen Fluoride Exercise Situation Manual (SitMan).
For more information about the exercise, please consult the following point of contact:
Exercise Director:
Glenn Paulson
Director
New Jersey Center for Public Health Preparedness
335 George Street
New Brunswick, NJ 08903
732-235-9704
paulsogl@umdnj .edu
304
CONTENTS
Steering Committee
ii
Preface
iii
Handling Instructions
iv
Contents
v
Introduction
1
Scenario Part 1
4
Scenario Part 2
8
Scenario Part 3
12
Appendix A: Area Map
15
Appendix B: Acute Exposure Guideline Levels (AEGLs)
16
Appendix C: Hydrogen Fluoride Fact Sheet
17
305
INTRODUCTION
Background
An incident involving release of hydrogen fluoride (HF) presents unique challenges to the
community because of its specific chemical and physical properties and the potential to cause
serious harm to human health. Gloucester County in southern New Jersey (NJ) is the location of
industrial facilities that store and use significant quantities of HF which is transported to the
facilities via rail or tractor-trailer. This presents a potential hazard to the Gloucester County
community and it is imperative that all partners in emergency response are aware of the
specialized medical treatment required for HF exposures. HF is unique in that the onset of
symptoms is often delayed. Symptoms include severe burns, inhalation hazards, and systemic
toxicity. As important as the medical treatment is the leadership on the county and local level
and the ability to effectively respond to the incident. Recent incidents in our region and across
the country reinforce the need for this initiative. March 2009 in Wind Gap, Pennsylvania, a
tractor-trailer carrying 33,000 pounds of hydrofluoric acid flipped over on a highway while
trying to avoid a deer. The subsequent leak was contained by state and local HAZMAT teams.
Approximately 5,000 residents were evacuated and the highway was closed. In January 2005,
outside of Pittsburgh, Pennsylvania, a train car filled with anhydrous hydrogen fluoride derailed
into the Allegheny River and released its contents.
Purpose
The purpose of this tabletop exercise (TTX) is to provide participants with an opportunity to
evaluate their current emergency response plans and capabilities for a response to an HF incident
in Gloucester County. The exercise will focus on key local and county capabilities in both
external and internal communication, coordination, and critical decision-making.
Scope
The scenario for the exercise will be a transportation incident involving a tractor-trailer carrying
HF. No specific Gloucester County industrial facility will be named or implicated in the
exercise. The exercise will focus on specific issues associated with a release of HF and will be
specifically aimed at hospital, public health, and first responder coordination of the incident.
The emphasis will be on coordination, problem identification, and problem resolution.
Decontamination issues will be covered, but the primary focus will be on response to and
management of the incident.
Target Capabilities
The National Planning Scenarios and establishment of the National Preparedness Priorities have
steered the focus of homeland security toward a capabilities-based planning approach.
Capabilities-based planning focuses on planning under uncertainty because the next danger or
disaster can never be forecast with complete accuracy. Therefore, capabilities-based planning
takes an all-hazards approach to planning and preparation that builds capabilities that can be
applied to a wide variety of incidents. States and urban areas use capabilities-based planning to
identify a baseline assessment of their homeland security efforts by comparing their current
capabilities against the Target Capabilities List (TCL) and the critical tasks of the Universal Task
306
List (UTL). This approach identifies gaps in current capabilities and focuses efforts on
identifying and developing priority capabilities and tasks for the jurisdiction. These priority
capabilities are articulated in the jurisdiction's homeland security strategy and Multiyear
Training and Exercise Plan, of which this exercise is a component.
The capabilities listed here have been selected by the HFEX Steering Committee from the
priority capabilities identified in Gloucester County's Multiyear Training and Exercise Plan.
These capabilities provide the foundation for development of the exercise design objectives and
scenario. The purpose of this exercise is to measure and validate performance of these
capabilities and their associated critical tasks. The selected target capabilities are:
•
•
•
•
•
•
•
Emergency Operations Center (EOC) Management
Responder Safety and Health
HazMat Response and Decontamination
Citizen Evacuation and/or Shelter-in-Place
Emergency Triage and Pre-Hospital Treatment
Medical Surge
Mass Care (Sheltering, Feeding, and Related Services)
Exercise Design Objectives
Exercise design objectives focus on improving understanding of a response concept, identifying
opportunities or problems, and achieving a change in attitude. This exercise will focus on the
following design objectives selected by the HFEX Steering Committee:
1. Assess and identify how to activate and maintain emergency communications
essential to support response to an HF incident in Gloucester County.
2. Demonstrate the ability to alert, mobilize, and activate personnel for emergency
response and maintain operations until the situation is brought under control.
3. Demonstrate the ability to mobilize and track equipment, people, and other resources
in support of emergency operations.
4. Identify and implement appropriate actions to protect emergency workers and the
public.
5. Demonstrate inter-agency (Gloucester County Health Department, hospitals, and first
responders) communication and cooperation in response to an HF incident in
Gloucester County.
Participants
•
•
Players. Players respond to the situation presented, based on expert knowledge of
response procedures, current plans and procedures, and insights derived from training.
Facilitators. Facilitators provide situation updates and moderate discussions. They also
provide additional information or resolve questions as required. Key Exercise Planning
307
•
•
•
Team members also may assist with facilitation as subject matter experts (SMEs) during
the TTX.
Evaluators. Evaluators observe and record the discussions during the exercise,
participate in the data analysis, and assist in drafting the after-action report. (AAR)
Subject Matter Experts (SME): SME are similar in role to an observer but may be
asked by participants specific questions about their agencies, policies, or area of
expertise.
Observers. Observers are not participants in the moderated discussion, they only
observe.
Exercise Structure
This tabletop exercise (TTX) will be a facilitated exercise with a multimedia presentation.
Players will participate at different times in the event response. At different points in the
response, there will be a multimedia update that summarizes key events occurring within that
time period. After the updates, participants will engage in a facilitated group discussion of
appropriate response issues and subsequent actions based on the scenario.
Exercise Guidelines
•
This TTX will be held in an open, low-stress, no-fault environment. Varying viewpoints,
even disagreements, are expected.
•
Respond on the basis of your knowledge of current plans and capabilities (i.e., you may
use only existing assets) and insights derived from your training.
•
Decisions are not precedent setting and may not reflect your organization's final position
on a given issue. This exercise is an opportunity to discuss and present multiple options
and possible solutions.
•
Issue identification is not as valuable as suggestions and recommended actions that could
improve response and preparedness efforts. Problem-solving efforts should be the focus.
Assumptions and Artificialities
In any exercise, assumptions and artificialities may be necessary to complete play in the time
allotted. During this exercise, the following apply:
•
•
•
The scenario is plausible, and events occur as they are presented.
There is no hidden agenda, and there are no trick questions.
All players receive information at the same time.
308
SCENARIO PART
1
May 25, 2010: 11:00 am 0 Hour
A tank truck exits Interstate 295 northbound at exit 18 in East Greenwich, NJ next to the truck
stop approaching Facility X. It is a sunny day and the temperature is 60 degrees with a 5 mph
wind blowing from the northwest. The driver of the tank truck swerves and overturns on the exit
ramp. A bystander calls 9-1-1 to report the overturned truck. Motorists are attempting to drive
through the accident area while gawking at the accident causing a back up of traffic. The truck
stop at the exit is busy with truckers and other patrons.
Injects:
•
The 9-1-1 dispatcher dispatches police, fire, and emergency medical services (EMS) to the
scene.
•
A white dense vapor with a pungent odor is coming from the tank truck.
309
May 25, 2010: 11:10 am +10 minutes
The tank truck has a UN 1052 label and placards indicating corrosive and poison. The truck
driver is unconscious. People from the truck stop and nearby businesses have been gathering
near the accident to see what is happening. Cars are lined up at the exit ramp attempting in vain
to get by.
Injects:
•
Bystanders at the truck stop have eye irritation, difficulty breathing and nausea.
•
A few of the spectators have collapsed.
310
Key Issues
•
•
•
•
•
Traffic congestions limiting movement of emergency vehicles.
Unknown condition of truck driver,
Substance leaking from tanker not identified.
Large numbers of people in the nearby surrounding area.
Reports of minor physical irritations of people exposed in the area of the accident.
Discussion Questions
Based on the information provided, participate in the discussion concerning the issues raised.
Identify any additional requirements, critical issues, decisions, or questions that should be
addressed at this time.
The following questions are provided as suggested general subjects that you may wish to address
as the discussion progresses. These questions are not meant to constitute a definitive list of
concerns to be addressed, nor is there a requirement to address every question.
Law Enforcement
•
•
•
•
•
At this stage of the response, what is the role of local law enforcement?
What actions would you take at this point? What are your priority action items at this point in
the response?
What, if any, additional local resources would you request at this time?
What are your perimeter and security concerns? How will these concerns be addressed?
What steps will be taken, and what resources will be required?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
Fire
•
•
•
•
At this stage of the response, what is the role of the fire departments present?
What actions would your agency take at this point? What are your priority action items at this
point in the response?
What, if any, additional local resources would you request at this time?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
Emergency Medical Services
•
•
•
•
•
At this stage of the response, what is the role of EMS?
What actions would you take at this point? What are your priority action items at this point in
the response?
What are your communication procedures at this point in the response?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
What, if any, additional local resources would you request at this time?
311
Hospitals
•
•
•
•
At this stage of the response, what is the role of the hospitals?
What actions would you take at this point? What are your priority action items at this point in
the response?
What are your communication procedures at this point in the response?
Is HF identified in the hospital hazard vulnerability analysis? If so, where is it on the list?
Emergency Management
•
•
•
•
Who is the Incident Commander? Which jurisdiction? Where is the command post? Where
is the staging area?
At this point in the response, what notifications would have been made, and by whom? How
would State agencies be notified of the situation?
What mutual aid agreements (MAAs) or memorandums of understanding (MOUs) do you
currently have in place that could be used for this response? Would mutual aid be requested
at this point? If so, from whom?
What are your primary safety concerns for citizens and first responders? What steps should
be taken to address these safety concerns? What resources may be required?
312
SCENARIO PART
2
May 25, 2010: 11:30 am +30 minutes
The tanker truck is continuing to expel a white vapor with a strong, irritating odor and it is
moving with the prevailing winds.
Injects:
•
The 9-1-1 call center has received a call from inside the truck stop (450 feet downwind) that
there are approximately 30 people outside on the ground and unresponsive. Of the people
inside, about twenty have difficulty breathing, are experiencing burns on their skin, eyes, and
in their noses and mouths. Other people inside are also vomiting and collapsing.
•
At East Coast Transportation Logistics, 900 feet downwind, the people gathered outside have
difficulty breathing and are also experiencing eye and skin irritation.
•
Five residents from Maple Avenue, further downwind, have called 9-1-1 reporting mild eye
irritations and a strong chemical odor.
May 25, 2010: 11:45 am +45 minutes
The Gloucester County Hazardous Materials (HazMat) Response Team arrives on the scene.
Inject:
•
HazMat team is on the scene, and the plume models are available.
313
Key Issues:
• Increasing numbers of people expressing common symptoms.
• Downwind range effect seems to be increasing.
• Anyone exposed at the outset is now non-ambulatory and unresponsive.
• Hazardous substance still leaking from tankers valve line.
• 9-1-1 calls are being made from people further down wind of the accident.
Discussion Questions
Based on the information provided, participate in the discussion concerning the issues raised.
Identify any additional requirements, critical issues, decisions, or questions that should be
addressed at this time.
The following questions are provided as suggested general subjects that you may wish to address
as the discussion progresses. These questions are not meant to constitute a definitive list of
concerns to be addressed, nor is there a requirement to address every question in this section.
Law Enforcement
•
•
•
•
•
What are your perimeter and security concerns at this point in the incident?
What role does your agency have in facilitating an orderly evacuation of the area?
What actions could be taken to improve the current situation and ensure that all responders
have access to the site and that EMS has an unobstructed path to area hospitals?
What are your priority action items for consideration at this point in the incident?
What challenges are you facing?
Fire
•
•
•
•
What are your priority action items for consideration at this point in the incident?
As additional resources arrive for this incident, where could you stage these assets?
What is the status of your communications?
What challenges are you facing?
Emergency Medical Services
•
•
•
•
•
•
What are your priority action items for consideration at this point in the incident?
What are your staging, triage and medical treatment concerns at this point?
What are your transportation concerns at this point? Where are you transporting patients to?
What are your communication procedures with the health care facilities?
What challenges are you facing?
What are the decontamination concerns with the transport of patients?
Hospitals
•
•
•
What are your priority action items for consideration at this point in the incident?
Hospital emergency operations plans?
What are your staffing concerns at this point?
314
What are your decontamination and treatment concerns at this point? Do you have the
necessary medications on site for HF treatment? If not, where is it available?
What challenges are you facing?
Emergency Management
Would the Emergency Operations Center (EOC) be activated at this point? If so, what is the
activation process, and how long would it take?
Would individual local agency EOCs or command centers be activated? If so, what is the
activation process, and how long would it take? How would these various entities
communicate?
How is the Emergency Operations Center (EOC) staffed at this point in the incident?
How would you prepare to staff for a possible extended activation? Are your current staffing
protocols sufficient to support extended activation? If not, how might this situation be
remedied?
What are the priority action items for consideration at this point in the incident?
What other agencies have been notified at this point in the response? What roles will these
agencies fulfill?
What challenges are you facing?
Do you anticipate any State or Federal resources arriving or being assigned to this incident?
If so, what issues regarding receipt, acceptance, tracking, management, and integration of
State and Federal resources need to be considered?
Who has the authority to order an evacuation? How large an area would you evacuate? What
plans are in place to facilitate an evacuation?
Is Facility X contacted? What information can they provide regarding the chemical and
medical concerns?
HazMat
•
•
•
•
•
•
What is the status of HazMat? When does the assessment team arrive?
What are the priority action items for consideration at this point in the incident?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
What are your site management and control concerns?
How will you coordinate information management and resource coordination?
What challenges are you facing?
Mass Care
•
•
•
•
•
What areas are being affected by the plume? What are the primary safety concerns for these
areas?
What are the decisions surrounding evacuation? What are the first considerations? Who will
make the decision to evacuate or shelter in place?
Who is responsible and will provide mass care services and resources to the shelter
population?
Have designated shelter sites been pre-established? How would the shelters be staffed?
What challenges are you facing?
315
Health Department
•
•
•
•
Is anyone checking the special needs registry?
What is your role in citizen evacuation/shelter in place?
What are your concerns regarding evacuation?
What are your communication concerns at this point in the response?
316
SCENARIO PART
3
May 25, 2010: 12:00 pm +60 minutes
HF is still leaking from the tanker and plume modeling has shown a down wind effect of up to
1.2 miles from the release site. At this time, both the indoor and outdoor concentrations at the
truck stop are above the Acute Exposure Guideline Level (AEGL)-3. Above the AEGL-3 any
concentration is life threatening.
Injects:
•
Everyone at the truck stop is unresponsive.
•
The outdoor concentration at the East Coast Transportation Logistics has also reached the
AEGL-3 and people outside are unresponsive.
•
The indoor concentration is below AEGL-2, indicating that exposed people will have longlasting adverse health effects and/or an impaired ability to escape.
•
More residents from Maple Ave, 1700 feet downwind, are calling 9-1-1 with more serious
complaints of eye and skin irritation, mild nausea, and strong odors. Similar calls are
coming in from residents on Whiskey Mill and Landing Roads, further downwind.
•
EMS has transported a total of 20 patients to local hospitals.
•
The local hospitals have reported that anywhere from 10 to 20 people have arrived on their
own to the emergency departments. They are not yet sure of their condition and could be
among the worried well.
•
9-1-1 is getting calls from neighborhood homes about whether to shelter in place or to
evacuate.
317
Key Issues:
•
•
•
•
•
•
HazMat is on scene however the leak still continues.
Acute Exposure Guideline Levels are increasing which will increase exposure and
debilitation of those exposed.
Range of exposure downwind is now at 1.2 miles from the accident site.
9-1-1 calls still being made downwind of the incident.
Numbers of patients transported to hospitals is increasing and currently at 20.
Hospitals are concerned about the worried well arriving at the emergency departments.
Questions
Based on the information provided, participate in the discussion concerning the issues raised.
Identify any additional requirements, critical issues, decisions, or questions that should be
addressed at this time.
The following questions are provided as suggested general subjects that you may wish to address
as the discussion progresses. These questions are not meant to constitute a definitive list of
concerns to be addressed, nor is there a requirement to address every question in this section.
Law Enforcement
•
•
•
•
•
What are your short- and long-term personnel needs for site security and the ongoing incident
investigation? Do you currently have enough personnel to meet these needs? If not, how
could the required additional personnel be obtained?
What material support will be required for these operations in the short and long terms?
What are your long-term perimeter and security plans? How long do you expect to have to
maintain a presence at the scene?
What are your priority action items at this point?
What challenges are you facing?
Fire
What are your short- and long-term personnel needs for the ongoing operations? Do you
currently have enough personnel to meet these needs? If not, how could the required
additional personnel be obtained?
What material support will be required for these operations in the short and long terms?
What are your priority action items at this point?
What challenges are you facing?
HazMat
What are your short- and long-term personnel needs for the ongoing search and rescue
operations? Do you currently have enough personnel to meet these needs? If not, how could
the required additional personnel be obtained?
What material support will be required to support these operations in the short and long
terms?
What are your priority action items at this point?
318
•
What challenges are you facing?
Emergency Management
•
•
What are your long-term staffing and support plans for your Emergency Operations Center
(EOC) or agency? Do you currently have enough personnel to meet these needs? If not, how
might they be obtained?
What are your priority action items at this point?
Emergency Medical Services
•
•
•
•
•
What are your short- and long-term personnel needs for ongoing medical treatment on site?
Do you currently have enough personnel to meet these needs? If not, how could the required
additional personnel be obtained?
What material support will be required to support these operations in the short and long
terms?
How long do you expect to have to maintain a presence at the scene?
What are your priority action items at this point?
What challenges are you facing?
Hospitals
•
•
•
•
•
•
What are your priority action items at this point?
What are your short- and long-term personnel needs for ongoing decontamination and
medical treatment? Do you currently have enough personnel to meet these needs? If not,
how could the required additional personnel be obtained?
What are your bed surge procedures?
What consideration has been given to the handling of the worried well?
What challenges are you facing?
What is the surge procedure for the hospitals?
Health Department
•
•
•
•
Special needs?
What are your concerns regarding safety and sanitation within the shelters?
What are the short and long term environmental concerns? What consideration should be
given to nearby waterways?
Special needs registry?
Mass Care
•
•
•
What are your short- and long-term personnel needs for ongoing shelter operations? Do you
currently have enough personnel to meet these needs? If not, how could the required
additional personnel be obtained?
How long will this be open?
How many more people can be expected?
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APPENDIX A: AREA MAP
320
APPENDIX
B: ACUTE EXPOSURE GUIDELINE LEVELS
(AEGLs)
AEGLs represent threshold exposure limits for the general public and are applicable to
emergency exposure periods ranging from 10 minutes to 8 hours. AEGL-2 and AEGL-3, and
AEGL-1 values as appropriate, will be developed for each of five exposure periods (10 and 30
minutes, 1 hour, 4 hours, and 8 hours) and will be distinguished by varying degrees of severity of
toxic effects. It is believed that the recommended exposure levels are applicable to the general
population including infants and children, and other individuals who may be susceptible. The
three AEGLs have been defined as follows:
AEGL-1 is the airborne concentration, expressed as parts per million or milligrams per cubic
meter (ppm or mg/m3) of a substance above which it is predicted that the general population,
including susceptible individuals, could experience notable discomfort, irritation, or certain
asymptomatic nonsensory effects. However, the effects are not disabling and are transient and
reversible upon cessation of exposure.
AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above which
it is predicted that the general population, including susceptible individuals, could experience
irreversible or other serious, long-lasting adverse health effects or an impaired ability to escape.
AEGL-3 is the airborne concentration (expressed as ppm or mg/m3) of a substance above which
it is predicted that the general population, including susceptible individuals, could experience
life-threatening health effects or death.
Airborne concentrations below the AEGL-1 represent exposure levels that can produce mild and
progressively increasing but transient and nondisabling odor, taste, and sensory irritation or
certain asymptomatic, nonsensory effects. With increasing airborne concentrations above each
AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects
described for each corresponding AEGL. Although the AEGL values represent threshold levels
for the general public, including susceptible subpopulations, such as infants, children, the
elderly, persons with asthma, and those with other illnesses, it is recognized that individuals,
subject to unique or idiosyncratic responses, could experience the effects described at
concentrations below the corresponding AEGL.
321
APPENDIX
C: HYDROGEN FLUORIDE FACT SHEET
Right to Know Hazardous Substance Fact Sheet
Emergency
Responders
Quick Reference
Common Name: HYDROGEN FLUORIDE
Synonyms: Fluoric Acid; HFA
CAS No: 7664-39-3
Molecular Formula: HF
RTK Substance No: 3759
Description: Colorless, fuming liquid or gas
HAZARD DATA
Hazard Rating
4 • Health
0 - Fire
1 - Reactivity
DOT#: UN 1052
ERG Guide #: 125
Hazard Class: 8
(Corrosive)
, Firefightlng
Reactivity
Hydrogen Fluoride is a noncombustible
liquid or gas.
Extinguish fire using an agent suitable for
tyPe °' surrounding fire.
POISONOUS GASES ARE PRODUCED
IN FIRE, including Fluorine.
Use water spray to keep fire exposed
containers cool.
Hydrogen Fluoride reacts violently with STRONG BASES (such as
SODIUM HYDROXIDE and POTASSIUM HYDROXIDE) and many
other compounds.
Hydrogen Fluoride reacts with WATER and STEAM to produce toxic
and corrosive gases.
Hydrogen Fluoride reacts with METALS (such as IRON
and STEEL) to produce flammable and explosive Hydrogen gas.
Hydrogen Fluoride is not compatible with OXIDIZING AGENTS
(such as PERCHLORATES, PEROXIDE. PERMANGANATES.
CHLORATES. NITRATES, CHLORINE, BROMINE and
FLUORINE); STRONG ACIDS (such as HYDROCHLORIC.
SULFURIC and NITRIC); AMINES; METAL SALTS; and SILICON
COMPOUNDS.
SPILL/LEAKS
PHYSICAL PROPERTIES
Isolation Distance:
Odor Threshold:
Spill: 100 meters (330 feet)
Fire: 1.600 meters (1 mile)
Flash Point:
Nonflammable
Vapor Density:
Vapor Pressure:
0.7 (air = 1)
760 mm Hg at 68°F (20°C)
Specific Gravity:
0.99 (water • 1)
If a gas leak, evacuate area and stop flow of gas. If source of
leak is a cylinder and the leak cannot be stopped in place,
remove the leaking cylinder to a safe place In the open air,
and repair leak or allow cylinder to empty.
If a liquid spill, allow to vaporize and disperse, or cover with
sodium carbonate or an equal mixture of soda ash and
slaked lime.
Water spray can be used to absorb Hydrogen Fluoride
vapors escaping from leaking containers of anhydrous
Hydrogen Fluoride Use water in flooding quantities
0.04 ppm
Water Solubility:
Miscible
Boiling Point:
Freezing Point:
67°F(19.4°C)
lonlzatlon Potential:
-117.4°F(-83°C)
15.98 eV
Molecular Weight:
20.1
EXPOSURE LIMITS
PROTECTIVE EQUIPMENT
ACGIH:
0.5 ppm, 8-hr TWA; 2 ppm, Ceiling
Gloves:
Barrier® (>8-hr breakthrough)
IDLH:
30 ppm
Coveralls:
Tychem® Responder® and TK: and Trellchem HPS (>8hr breakthrough)
Respirator:
SCBA
The Protective Action Criteria values are;
PAC-1 * 1 ppm
PAC-3 = 44 ppm
PAC-2 = 24 ppm
FIRST AID AND DECONTAMINATION
HEALTH EFFECTS
Eyes:
Skin:
Inhalation:
Severe irritation, bums and possible eye
damage
Irritation and severe burns
Nose, throat and lung irritation with
coughing, and severe shortness of
breath (pulmonary edema)
Headache, dizziness, weakness, and
convulsions
Remove the person from exposure.
Flush eyes with large amounts of water for at least 30 minutes. Remove
contact lenses if worn. Seek medical attention immediately.
Immediately flush with large amounts of water. Apply 2.5% Calcium
Gluconate gel to the affected skin. Seek medical assistance
immediately.
Begin artificial respiration If breathing has stopped and CPR if necessary.
Transfer promptly to a medical facility.
Medical observation is recommended as symptoms may be delayed.
322
HOT WASH GUIDE
The facilitators of the HFEX will run the "hot wash" after the TTX. The hot wash will be an
unstructured discussion for the players only. Evaluators will submit their comments to lead
evaluator and observers will be able to comment on the participant feedback form. Below are
the talking points for the hot wash.
•
Overall thoughts/comments
•
Strengths
•
Areas that need improvement
323
02 HFEX Player's Manual
SITUATION MANUAL
HFEX-
HYDROGEN FLUORIDE EXERCISE
EXERCISE DATE
: 05/25/2010
324
STEERING COMMITTEE
Diane Anderson
New Jersey Hospital Association
Rebecca Baron
New Jersey Center for Public Health Preparedness
Robert Brownlee
New Jersey Department of Health and Senior Services
Jack DeAngelo
Gloucester County Office of Emergency Management
George DiFerdinando
New Jersey Center for Public Health Preparedness
William Donovan
Gloucester County Prosecutor's Office
Mitchell Erickson
United States Department of Homeland Security
Bryan Everingham
New Jersey State Police
Kevin Hayden
New Jersey Department of Health and Senior Services
Daniel McFadden
New Jersey Department of Health and Senior Services
Glenn Paulson
New Jersey Center for Public Health Preparedness
Christine Poulsen
New Jersey Center for Public Health Preparedness
Dennis Quinn
New Jersey Office of Homeland Security and Preparedness
Thomas Rafferty
New Jersey State Police
Dennis Sample
New Jersey Office of Homeland Security and Preparedness
Robert Van Fossen
New Jersey Department of Environmental Protection
Scott Woodside
Gloucester County Department of Health and Senior Services
325
PREFACE
The HFEX is sponsored by the New Jersey Center for Public Health Preparedness (NJCPHP).
This Situation Manual (SitMan) was produced with input, advice, and assistance from the HFEX
Steering Committee, which followed guidance set forth by the U.S. Department of Homeland
Security (DHS) Homeland Security Exercise and Evaluation Program (HSEEP).
The HFEX SitMan provides exercise participants with all the necessary tools for their roles in
the exercise. It is tangible evidence of Gloucester County's commitment to ensure public safety
through collaborative partnerships that will prepare it to respond to any emergency.
The HFEX is an unclassified exercise. Control of exercise information is based on public
sensitivity regarding the nature of the exercise rather than actual exercise content. Some exercise
material is intended for the exclusive use of exercise planners, facilitators, and evaluators, but
players may view other materials that are necessary to their performance. All exercise
participants may view the SitMan.
All exercise participants should use appropriate guidelines to ensure proper control of
information within their areas of expertise and protect this material in accordance with current
jurisdictional directives. Public release of exercise materials to third parties is at the discretion of
the HFEX Steering Committee.
326
HANDLING INSTRUCTIONS
The title of this document is the HFEX- Hydrogen Fluoride Exercise Situation Manual (SitMan).
For more information about the exercise, please consult the following point of contact:
Exercise Director:
Glenn Paulson
Director
New Jersey Center for Public Health Preparedness
335 George Street
New Brunswick, NJ 08903
732-235-9704
paulsogl@umdnj .edu
327
CONTENTS
Steering Committee
ii
Preface
iii
Handling Instructions
iv
Contents
v
Introduction
1
Scenario Part 1
4
Scenario Part 2
8
Scenario Part 3
12
Appendix A: Area Map
15
Appendix B: Acute Exposure Guideline Levels (AEGLs)
16
Appendix C: Hydrogen Fluoride Fact Sheet
17
328
INTRODUCTION
Background
An incident involving release of hydrogen fluoride (HF) presents unique challenges to the
community because of its specific chemical and physical properties and the potential to cause
serious harm to human health. Gloucester County in southern New Jersey (NJ) is the location of
industrial facilities that store and use significant quantities of HF which is transported to the
facilities via rail or tractor-trailer. This presents a potential hazard to the Gloucester County
community and it is imperative that all partners in emergency response are aware of the
specialized medical treatment required for HF exposures. HF is unique in that the onset of
symptoms is often delayed. Symptoms include severe burns, inhalation hazards, and systemic
toxicity. As important as the medical treatment is the leadership on the county and local level
and the ability to effectively respond to the incident. Recent incidents in our region and across
the country reinforce the need for this initiative. March 2009 in Wind Gap, Pennsylvania, a
tractor-trailer carrying 33,000 pounds of hydrofluoric acid flipped over on a highway while
trying to avoid a deer. The subsequent leak was contained by state and local Hazardous
Materials (HazMat) teams. Approximately 5,000 residents were evacuated and the highway was
closed. In January 2005, outside of Pittsburgh, Pennsylvania, a train car filled with anhydrous
hydrogen fluoride derailed into the Allegheny River and released its contents.
Purpose
The purpose of this tabletop exercise (TTX) is to provide participants with an opportunity to
evaluate their current emergency response plans and capabilities for a response to an HF incident
in Gloucester County. The exercise will focus on key local and county capabilities in both
external and internal communication, coordination, and critical decision-making.
Scope
The scenario for the exercise will be a transportation incident involving a tractor-trailer carrying
HF. No specific Gloucester County industrial facility will be named or implicated in the
exercise. The exercise will focus on specific issues associated with a release of HF and will be
specifically aimed at hospital, public health, and first responder coordination of the incident.
The emphasis will be on coordination, problem identification, and problem resolution.
Decontamination issues will be covered, but the primary focus will be on response to and
management of the incident.
Target Capabilities
The National Planning Scenarios and establishment of the National Preparedness Priorities have
steered the focus of homeland security toward a capabilities-based planning approach.
Capabilities-based planning focuses on planning under uncertainty because the next danger or
disaster can never be forecast with complete accuracy. Therefore, capabilities-based planning
takes an all-hazards approach to planning and preparation that builds capabilities that can be
applied to a wide variety of incidents. States and urban areas use capabilities-based planning to
identify a baseline assessment of their homeland security efforts by comparing their current
capabilities against the Target Capabilities List (TCL) and the critical tasks of the Universal Task
329
List (UTL). This approach identifies gaps in current capabilities and focuses efforts on
identifying and developing priority capabilities and tasks for the jurisdiction. These priority
capabilities are articulated in the jurisdiction's homeland security strategy and Multiyear
Training and Exercise Plan, of which this exercise is a component.
The capabilities listed here have been selected by the HFEX Steering Committee from the
priority capabilities identified in Gloucester County's Multiyear Training and Exercise Plan.
These capabilities provide the foundation for development of the exercise design objectives and
scenario. The purpose of this exercise is to measure and validate performance of these
capabilities and their associated critical tasks. The selected target capabilities are:
•
•
•
•
•
•
•
Emergency Operations Center (EOC) Management
Responder Safety and Health
HazMat Response and Decontamination
Citizen Evacuation and/or Shelter-in-Place
Emergency Triage and Pre-Hospital Treatment
Medical Surge
Mass Care (Sheltering, Feeding, and Related Services)
Exercise Design Objectives
Exercise design objectives focus on improving understanding of a response concept, identifying
opportunities or problems, and achieving a change in attitude. This exercise will focus on the
following design objectives selected by the HFEX Steering Committee:
1. Assess and identify how to activate and maintain emergency communications
essential to support response to an HF incident in Gloucester County.
2. Demonstrate the ability to alert, mobilize, and activate personnel for emergency
response and maintain operations until the situation is brought under control.
3. Demonstrate the ability to mobilize and track equipment, people, and other resources
in support of emergency operations.
4. Identify and implement appropriate actions to protect emergency workers and the
public.
5. Demonstrate inter-agency (Gloucester County Health Department, hospitals, and first
responders) communication and cooperation in response to an HF incident in
Gloucester County.
Participants
•
Players. Players respond to the situation presented, based on expert knowledge of
response procedures, current plans and procedures, and insights derived from training.
•
Facilitators. Facilitators provide situation updates and moderate discussions. They also
provide additional information or resolve questions as required. Key Exercise Planning
330
•
•
•
Team members also may assist with facilitation as subject matter experts (SMEs) during
the TTX.
Evaluators. Evaluators observe and record the discussions during the exercise,
participate in the data analysis, and assist in drafting the after-action report. (AAR)
Subject Matter Experts (SME): SME are similar in role to an observer but may be
asked by participants specific questions about their agencies, policies, or area of
expertise.
Observers. Observers are not participants in the moderated discussion, they only
observe.
Exercise Structure
This tabletop exercise will be a facilitated exercise with a multimedia presentation. At different
points in the response, there will be a multimedia update that summarizes key events occurring
within that time period. After the updates, players will engage in a facilitated group discussion of
appropriate response issues and subsequent actions based on the scenario.
Exercise Guidelines
•
This TTX will be held in an open, low-stress, no-fault environment. Varying viewpoints,
even disagreements, are expected.
•
Respond on the basis of your knowledge of current plans and capabilities (i.e., you may
use only existing assets) and insights derived from your training.
•
Decisions are not precedent setting and may not reflect your organization's final position
on a given issue. This exercise is an opportunity to discuss and present multiple options
and possible solutions.
•
Issue identification is not as valuable as suggestions and recommended actions that could
improve response and preparedness efforts. Problem-solving efforts should be the focus.
Assumptions and Artificialities
In any exercise, assumptions and artificialities may be necessary to complete play in the time
allotted. During this exercise, the following apply:
•
•
•
The scenario is plausible, and events occur as they are presented.
There is no hidden agenda, and there are no trick questions.
All players receive information at the same time.
331
SCENARIO PART
1
May 25, 2010: 11:00am 0 Hour
A tank truck exits Interstate 295 northbound at exit 18 in East Greenwich, NJ next to the truck
stop approaching Facility X. It is a sunny day and the temperature is 60 degrees with a 5 mph
wind blowing from the northwest. The driver of the tank truck swerves and overturns on the exit
ramp. A bystander calls 9-1-1 to report the overturned truck. Motorists are attempting to drive
through the accident area while gawking at the accident causing a back up of traffic. The truck
stop at the exit is busy with truckers and other patrons.
332
May 25, 2010: 11:10am (+10 minutes)
The tank truck has a UN 1052 label and placards indicating corrosive and poison. The truck
driver is unconscious. People from the truck stop and nearby businesses have been gathering
near the accident to see what is happening. Cars are lined up at the exit ramp attempting in vain
to get by.
333
Key Issues
•
•
•
•
•
Traffic congestions limiting movement of emergency vehicles.
Unknown condition of truck driver.
Substance leaking from tanker not identified.
Large numbers of people in the nearby surrounding area.
Reports of minor physical irritations of people exposed in the area of the accident.
Discussion Questions
Based on the information provided, participate in the discussion concerning the issues raised.
Identify any additional requirements, critical issues, decisions, or questions that should be
addressed at this time.
The following questions are provided as suggested general subjects that you may wish to address
as the discussion progresses. These questions are not meant to constitute a definitive list of
concerns to be addressed, nor is there a requirement to address every question.
Law Enforcement
•
•
•
•
•
At this stage of the response, what is the role of local law enforcement?
What actions would you take at this point? What are your priority action items at this point in
the response?
What, if any, additional local resources would you request at this time?
What are your perimeter and security concerns? How will these concerns be addressed?
What steps will be taken, and what resources will be required?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
Fire
•
•
•
•
At this stage of the response, what is the role of the fire departments present?
What actions would your agency take at this point? What are your priority action items at this
point in the response?
What, if any, additional local resources would you request at this time?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
Emergency Medical Services
•
•
•
•
•
At this stage of the response, what is the role of EMS?
What actions would you take at this point? What are your priority action items at this point in
the response?
What are your communication procedures at this point in the response?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
What, if any, additional local resources would you request at this time?
334
Hospitals
•
•
•
•
At this stage of the response, what is the role of the hospitals?
What actions would you take at this point? What are your priority action items at this point in
the response?
What are your communication procedures at this point in the response?
Is HF identified in the hospital hazard vulnerability analysis? If so, where is it on the list?
Emergency Management
•
•
•
•
Who is the Incident Commander? Which jurisdiction? Where is the command post? Where
is the staging area?
At this point in the response, what notifications would have been made, and by whom? How
would State agencies be notified of the situation?
What mutual aid agreements (MAAs) or memorandums of understanding (MOUs) do you
currently have in place that could be used for this response? Would mutual aid be requested
at this point? If so, from whom?
What are your primary safety concerns for citizens and first responders? What steps should
be taken to address these safety concerns? What resources may be required?
335
SCENARIO PART
2
May 25, 2010: 11:30am (+30 minutes)
The tank truck is continuing to expel a white vapor with a strong, irritating odor and it is moving
with the prevailing winds.
May 25, 2010: 11:45am (+45 minutes)
The Gloucester County Hazardous Materials (HazMat) Response Team arrives on the scene.
336
Key Issues:
Increasing numbers of people expressing common symptoms.
Downwind range effect seems to be increasing.
Anyone exposed at the outset is now non-ambulatory and unresponsive.
Hazardous substance still leaking from tankers valve line.
9-1-1 calls are being made from people further down wind of the accident.
Discussion Questions
Based on the information provided, participate in the discussion concerning the issues raised.
Identify any additional requirements, critical issues, decisions, or questions that should be
addressed at this time.
The following questions are provided as suggested general subjects that you may wish to address
as the discussion progresses. These questions are not meant to constitute a definitive list of
concerns to be addressed, nor is there a requirement to address every question in this section.
Law Enforcement
•
•
•
•
•
What are your perimeter and security concerns at this point in the incident?
What role does your agency have in facilitating an orderly evacuation of the area?
What actions could be taken to improve the current situation and ensure that all responders
have access to the site and that EMS has an unobstructed path to area hospitals?
What are your priority action items for consideration at this point in the incident?
What challenges are you facing?
Fire
•
•
•
•
What are your priority action items for consideration at this point in the incident?
As additional resources arrive for this incident, where could you stage these assets?
What is the status of your communications?
What challenges are you facing?
Emergency Medical Services
•
•
•
•
•
•
What are your priority action items for consideration at this point in the incident?
What are your staging, triage and medical treatment concerns at this point?
What are your transportation concerns at this point? Where are you transporting patients to?
What are your communication procedures with the health care facilities?
What challenges are you facing?
What are the decontamination concerns with the transport of patients?
Hospitals
•
•
•
What are your priority action items for consideration at this point in the incident?
Hospital emergency operations plans?
What are your staffing concerns at this point?
337
What are your decontamination and treatment concerns at this point? Do you have the
necessary medications on site for HF treatment? If not, where is it available?
What challenges are you facing?
Emergency Management
Would the Emergency Operations Center (EOC) be activated at this point? If so, what is the
activation process, and how long would it take?
Would individual local agency EOCs or command centers be activated? If so, what is the
activation process, and how long would it take? How would these various entities
communicate?
How is the Emergency Operations Center (EOC) staffed at this point in the incident?
How would you prepare to staff for a possible extended activation? Are your current staffing
protocols sufficient to support extended activation? If not, how might this situation be
remedied?
What are the priority action items for consideration at this point in the incident?
What other agencies have been notified at this point in the response? What roles will these
agencies fulfill?
What challenges are you facing?
Do you anticipate any State or Federal resources arriving or being assigned to this incident?
If so, what issues regarding receipt, acceptance, tracking, management, and integration of
State and Federal resources need to be considered?
Who has the authority to order an evacuation? How large an area would you evacuate? What
plans are in place to facilitate an evacuation?
Is Facility X contacted? What information can they provide regarding the chemical and
medical concerns?
HazMat
•
•
•
•
•
•
What is the status of HazMat? When does the assessment team arrive?
What are the priority action items for consideration at this point in the incident?
What are your primary safety concerns for your personnel? What steps should be taken to
address these safety concerns? What resources may be required?
What are your site management and control concerns?
How will you coordinate information management and resource coordination?
What challenges are you facing?
Mass Care
•
•
•
•
•
What areas are being affected by the plume? What are the primary safety concerns for these
areas?
What are the decisions surrounding evacuation? What are the first considerations? Who will
make the decision to evacuate or shelter in place?
Who is responsible and will provide mass care services and resources to the shelter
population?
Have designated shelter sites been pre-established? How would the shelters be staffed?
What challenges are you facing?
338
Health Department
•
•
•
•
Is anyone checking the special needs registry?
What is your role in citizen evacuation/shelter in place?
What are your concerns regarding evacuation?
What are your communication concerns at this point in the response?
339
SCENARIO PART
3
May 25, 2010: 12:00pm (+60 minutes)
HF is still leaking from the tanker and plume modeling has shown a down wind effect of up to
l .2 miles from the release site. At this time, both the indoor and outdoor concentrations at the
truck stop are above the Acute Exposure Guideline Level (AEGL)-3. Above the AEGL-3 any
concentration is life threatening.
340
Key Issues:
•
•
•
•
•
•
HazMat is on scene however the leak still continues.
Acute Exposure Guideline Levels are increasing which will increase exposure and
debilitation of those exposed.
Range of exposure downwind is now at 1.2 miles from the accident site.
9-1-1 calls still being made downwind of the incident.
Numbers of patients transported to hospitals is increasing and currently at 20.
Hospitals are concerned about the worried well arriving at the emergency departments.
Questions
Based on the information provided, participate in the discussion concerning the issues raised.
Identify any additional requirements, critical issues, decisions, or questions that should be
addressed at this time.
The following questions are provided as suggested general subjects that you may wish to address
as the discussion progresses. These questions are not meant to constitute a definitive list of
concerns to be addressed, nor is there a requirement to address every question in this section.
Law Enforcement
•
•
•
•
•
What are your short- and long-term personnel needs for site security and the ongoing incident
investigation? Do you currently have enough personnel to meet these needs? If not, how
could the required additional personnel be obtained?
What material support will be required for these operations in the short and long terms?
What are your long-term perimeter and security plans? How long do you expect to have to
maintain a presence at the scene?
What are your priority action items at this point?
What challenges are you facing?
Fire
What are your short- and long-term personnel needs for the ongoing operations? Do you
currently have enough personnel to meet these needs? If not, how could the required
additional personnel be obtained?
What material support will be required for these operations in the short and long terms?
What are your priority action items at this point?
What challenges are you facing?
HazMat
What are your short- and long-term personnel needs for the ongoing search and rescue
operations? Do you currently have enough personnel to meet these needs? If not, how could
the required additional personnel be obtained?
What material support will be required to support these operations in the short and long
terms?
What are your priority action items at this point?
341
•
What challenges are you facing?
Emergency Management
•
•
What are your long-term staffing and support plans for your Emergency Operations Center
(EOC) or agency? Do you currently have enough personnel to meet these needs? If not, how
might they be obtained?
What are your priority action items at this point?
Emergency Medical Services
•
•
•
•
•
What are your short- and long-term personnel needs for ongoing medical treatment on site?
Do you currently have enough personnel to meet these needs? If not, how could the required
additional personnel be obtained?
What material support will be required to support these operations in the short and long
terms?
How long do you expect to have to maintain a presence at the scene?
What are your priority action items at this point?
What challenges are you facing?
Hospitals
•
•
•
•
•
•
What are your priority action items at this point?
What are your short- and long-term personnel needs for ongoing decontamination and
medical treatment? Do you currently have enough personnel to meet these needs? If not,
how could the required additional personnel be obtained?
What are your bed surge procedures?
What consideration has been given to the handling of the worried well?
What challenges are you facing?
What is the surge procedure for the hospitals?
Health Department
•
•
•
•
Special needs?
What are your concerns regarding safety and sanitation within the shelters?
What are the short and long term environmental concerns? What consideration should be
given to nearby waterways?
Special needs registry?
Mass Care
•
•
•
What are your short- and long-term personnel needs for ongoing shelter operations? Do you
currently have enough personnel to meet these needs? If not, how could the required
additional personnel be obtained?
How long will this be open?
How many more people can be expected?
342
APPENDIX A: AREA MAP
a }K. \
AWARE
"*&&? ...LV1
**
343
APPENDIX
B: ACUTE EXPOSURE GUIDELINE LEVELS
(AEGLs)
AEGLs represent threshold exposure limits for the general public and are applicable to
emergency exposure periods ranging from 10 minutes to 8 hours. AEGL-2 and AEGL-3, and
AEGL-1 values as appropriate, will be developed for each of five exposure periods (10 and 30
minutes, 1 hour, 4 hours, and 8 hours) and will be distinguished by varying degrees of severity of
toxic effects. It is believed that the recommended exposure levels are applicable to the general
population including infants and children, and other individuals who may be susceptible. The
three AEGLs have been defined as follows:
AEGL-1 is the airborne concentration, expressed as parts per million or milligrams per cubic
meter (ppm or mg/m3) of a substance above which it is predicted that the general population,
including susceptible individuals, could experience notable discomfort, irritation, or certain
asymptomatic nonsensory effects. However, the effects are not disabling and are transient and
reversible upon cessation of exposure.
AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above which
it is predicted that the general population, including susceptible individuals, could experience
irreversible or other serious, long-lasting adverse health effects or an impaired ability to escape.
AEGL-3 is the airborne concentration (expressed as ppm or mg/m3) of a substance above which
it is predicted that the general population, including susceptible individuals, could experience
life-threatening health effects or death.
Airborne concentrations below the AEGL-1 represent exposure levels that can produce mild and
progressively increasing but transient and nondisabling odor, taste, and sensory irritation or
certain asymptomatic, nonsensory effects. With increasing airborne concentrations above each
AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects
described for each corresponding AEGL. Although the AEGL values represent threshold levels
for the general public, including susceptible subpopulations, such as infants, children, the
elderly, persons with asthma, and those with other illnesses, it is recognized that individuals,
subject to unique or idiosyncratic responses, could experience the effects described at
concentrations below the corresponding AEGL.
344
APPENDIX
C:
HYDROGEN FLUORIDE FACT SHEET
Right to Know Hazardous Substance Fact Sheet
Emergency
Respondent
Quick Reference
Common Name: HYDROGEN FLUORIDE
Synonyms: Fluoric Acid; HFA
CAS No: 7664-39-3
Molecular Formula: HF
RTK Substance No: 3759
Description: Colorless, fuming liquid or gas
HAZARD DATA
Hazard Rating
Firefighting
Reactivity
4 • Health
Hydrogen Fluoride is a noncombustible
liquid or gas.
Extinguish fire using an agent suitable for
^pe °' surrounding fire.
POISONOUS GASES ARE PRODUCED
IN FIRE, including Fluorine.
Use water spray to keep Are exposed
containers cool.
Hydrogen Fluoride reacts violently with STRONG BASES (such as
SODIUM HYDROXIDE and POTASSIUM HYDROXIDE) and many
other compounds.
Hydrogen Fluoride reacts with WATER and STEAM to produce toxic
and corrosive gases.
Hydrogen Fluoride reacts with METALS (such as IRON
and STEEL) to produce flammable and explosive Hydrogen gas.
Hydrogen Fluoride is not compatible with OXIDIZING AGENTS
(such as PERCHLORATES. PEROXIDE, PERMANGANATES.
CHLORATES. NITRATES, CHLORINE. BROMINE and
FLUORINE); STRONG ACIDS (such as HYDROCHLORIC,
SULFURIC and NITRIC); AMINES; METAL SALTS; and SILICON
COMPOUNDS
0 • Fire
1 - Reactivity
DOT* UN 1052
ERG Guide U: 125
Hazard Class: 8
(Corrosive)
SPILL/LEAKS
PHYSICAL PROPERTIES
Isolation Distance:
Odor Threshold:
Spill: 100 meters (330 feet)
Fire: 1,600 meters (1 mile)
Flash Point:
Nonflammable
Vapor Density:
Vapor Pressure:
0.7 (air = 1)
760 mm Hg at 68°F (20°C)
Specific Gravity:
0.99 (water « 1)
If a gas leak, evacuate area and stop flow of gas. If source of
teak is a cylinder and the leak cannot be stopped in place,
remove the leaking cylinder to a safe place in the open air.
and repair leak or allow cylinder to empty.
If a liquid spill, allow to vaporize and disperse, or cover with
sodium carbonate or an equal mixture of soda ash and
slaked lime.
Water spray can be used to absorb Hydrogen Fluoride
vapors escaping from leaking containers of anhydrous
Hydrogen Fluoride Use water in flooding quantities.
Water Solubility:
Miscible
Boiling Point:
Freezing Point:
67°F(19.4°C)
-117.4°F(-83°C)
lonization Potential:
Molecular Weight:
20.1
0.5 ppm 8-hr TWA; 2 ppm. Celling
30 ppm
The Protective Action Criteria values are:
PAC-3 • 44 ppm
PAC-1 • 1 ppm
PAC-2 = 24 ppm
Gloves:
Barrier® (>8-hr breakthrough)
Coveralls:
Tychem® Responder® and TK; and Trellchem HPS (>8hr breakthrough)
Respirator:
SCBA
HEALTH EFFECTS
Eyes:
Skin:
Inhalation:
Severe irritation, bums and possible eye
damage
Irritation and severe burns
Nose, throat and lung irritation with
coughing, and severe shortness of
breath (pulmonary edema)
Headache, dizziness, weakness, and
convulsions
15 98eV
PROTECTIVE EQUIPMENT
EXPOSURE LIMITS
ACGIH:
IDLH:
0.04 ppm
FIRST AID AND DECONTAMINATION
Remove the person from exposure.
Flush eyes with large amounts of water for at least 30 minutes. Remove
contact lenses if worn. Seek medical attention immediately.
Immediately flush with large amounts of water. Apply 2.5% Calcium
Gluconate gel to the affected skin. Seek medical assistance
immediately
Begin artificial respiration if breathing has stopped and CPR if necessary.
Transfer promptly to a medical facility.
Medical observation is recommended as symptoms may be delayed.
345
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05 HFEX Evaluation Abstract - Final
HFEX- HYDROGEN FLUORIDE EXERCISE PARTICIPANT FEEDBACK
FORM SUMMARY
"HFEX- Hydrogen Fluoride Exercise" was held on May 24, 2010. The exercise was held at the
Exxon-Mobil Technology Center in Paulsboro, NJ. There were a total of 101 participants and 40
(39.6%) completed participant feedback forms. Of the participants who completed the forms; 3
(8%) were evaluators, 18 (45%) were observers, 17 (43%) were players, and 2 (5%) were subject
matter experts. The participants who completed the feedback forms were from a wide range of
agencies that were at the exercise. Although it was optional, 27 of the participants wrote in their
names and agencies.
The participant feedback form was altered from the Homeland Security Exercise and Evaluation
Program (HSEEP) participant feedback form. The form had three sections; Part I:
Recommendations and Corrective Actions, Part II: Assessment of Training Session and Exercise
Design and Conduct, and Part III: Participant Feedback. The questions in Part II were written
using a Likert scale of 1 to 5, with 5 being the highest value.
The first question in Part I asked the top strengths of the exercise. Communications, interagency
cooperation, use of ICS and unified command were the top three strengths that were listed.
Participants also noted the brainstorming of ideas, the participants were varied and
representative, a comfortable exercise environment, the players had strong knowledge of their
roles and responsibility, and a realistic scenario.
The second question in Part I asked the top three areas that need improvement.
The offensive actions of the HazMat team in controlling the spill, chain of command, and
communications between agencies could be improved. Other comments including better
communication with the hospitals, treatment of causalities, more discussion on how to protect
first responders, and how to handle special needs populations.
The third question in Part I asked, " Identify the action steps that should be taken to address the
issues you identified above. For each action step, indicate if it is a high, medium, or low
priority." The corrective actions identified were:
High Priority
Medium Priority
Low Priority
County and local entities
need to be able to
communicate directly.
Expect that bystanders will
be involved, call 9-1-1, and
become victims themselves,
and work the best solutions
based on that more realistic
model.
Evaluate ability to save lives
first before logistical and
jurisdictional issues.
Understanding scale of
incident
Fatality management.
Mitigation of the vapor/leak
Accuracy of the
notifications.
370
Good understanding of task
forces plus resource needs
Realize that initial units
maybe affected and become
part of the problem
Recovery attempts
Working of multiple PIO's
together
Identify benefits and
concerns upon evacuations of
schools and senior housing
areas and other special needs
populations.
The fourth question in Part I asked, "List the policies, plans, and procedures that should be
reviewed, revised, or developed. For each, indicate if it is a high, medium, or low priority." The
items for review identified were:
High Priority
Medium Priority
Low Priority
Evaluate behavioral science
research and establish what
people are likely to do and
incorporate it into conops
Re-evaluate communication
policies across counties and
agencies
Re-evaluate priorities on the
incident and make sure the
order of operations and
urgency of patient care is
realistic
All SOPs should be
reviewed.
Preplan for extreme hazard
chemicals found in
jurisdiction, train all PD and
FD to id chemical as first
step.
Contacting immediate area
(truck stop) and informing
them to evacuate due to
hazardous material spill, save
lives.
Offensive action to minimize
incident
Special needs and shelter
operations
Overturned tanker (HazMat
suspected) protocol.
ARC needs from local
municipalities, LE, EMS
Department of Health's' role
in similar situation, better
identified
Establishing a common
meeting place for agency
representatives.
Plume modeling for several
other scenarios
Fatality management- ME,
LE, and responder
partnership
Realistic plan to coordinate
required evacuations with
offensive actions that can be
taken.
371
Part II of the form was Assessment of Training Session and Exercise Design and Conduct.
Participants rated their overall assessment of the exercise on a scale from 1 to 5, with 1
indicating strong disagreement with the statement, 5 indicating strong agreement, and 7
indicating don't know or not applicable. All of the means for the assessment factors were greater
than 4, with the highest being a 4.68. The assessment factor with the greatest average and most
number of 5's was on the trainers. The weakest of the factors was on the Situation Manual and
its use during the exercise.
Strongly Disagree
->
Strongly Agree
Assessment Factor
Total
1
2
3
4
5
Mean
The morning training session
prepared the participants for the
tabletop exercise and discussion.
N
%
0
0.0
1
0.03
4
0.10
14
0.35
21
0.53
40
1.00
4.38
The trainers were knowledgeable on
the topic and their presentations were
understandable.
N
%
0
0.0
1
0.03
1
0.03
8
0.20
30
0.75
40
1.00
4.68
The exercise was well structured and
organized.
N
%
0
0.0
0
0.0
3
0.08
16
0.40
21
0.53
40
1.00
4.45
The exercise scenario was plausible
and realistic.
N
%
0
0.0
1
0.03
4
0.10
18
0.45
17
0.43
40
1.00
4.28
The multimedia presentation helped
the participants understand and
become engaged in the scenario.
N
%
0
0.0
2
0.05
3
0.08
18
0.45
17
0.43
40
1.00
4.28
The facilitators were knowledgeable
about the material, kept the exercise
on target, and were sensitive to group
dynamics.
N
%
0
0.0
0
0.0
2
0.05
19
0.48
19
0.48
40
1.00
4.43
The Situation Manual used during the
exercise was a valuable tool
throughout the exercise.
N
%
0
0.0
0
0.0
11
0.29
14
0.37
13
0.34
40
1.00
4.05
Participation in the exercise was
appropriate for someone in my
position.
N
%
0
0.0
0
0.0
2
0.05
15
0.38
23
0.58
40
1.00
4.53
The participants included the right
people in terms of level and mix of
disciplines.
N
%
0
0.0
0
0.0
0
0.0
14
0.35
26
0.65
40
1.00
4.65
372
In part III - Participant Feedback of the exercise evaluation the participants were asked what
changes they would make to the overall exercise and to provide recommendations for how to
improve future exercises.
Overall the exercise was well received. Some of the positive comments on the exercise were that
it was well planned, well organized, and well done; the facilitators did a good job moving along
the scenario; the facility was good; and that it was a great opportunity for multiple agencies to
collaborate.
Several participants made recommendations for how to change and enhance future exercises.
Some people stated that the scenario and timeline could have more realistically reflected the
numbers of victims and response timelines. One person felt that the numbers of victims were
underestimated due to the fact that the exercise operated on what the people should do in such a
situation and not what they will do. Another person felt that it is important to push the tabletop
participants to be realistic in their time frames for response and completion of tasks.
Another area where participants recommended changes the focus of the exercise. Some
participants recommended focusing more on mitigation and medical care/treatment and less on
the HazMat response. Other suggestions were to discuss more in depth hot zone rescue, level A
entry and shelter options. One participant suggested that if the tabletop content had been
diminished there would have been more time to cover other areas. Another suggestion was to
make the exercise in two phases, one like HFEX at 0-60 min and a second one at 12 or 24 hours
to evaluate fatigue, shift change, etc. Finally, a participant stated that the facilitators need to
control the focus of the exercise as some players by nature were more engaged and dominated
the exercise with their specific concerns and responsibilities.
The exercise was accompanied by a PowerPoint presentation, but some participants
recommended further exercise aides, such as: A map in the SitMan similar in scale to the map
used in the PowerPoint presentation, access to the MSDS, Radio transmissions to support injects,
a scribe taking notes for everybody to more easily follow the scenario and activities and
responsibilities. One participant recommended that a brief instruction on how tabletop exercises
proceed prior to start would get the exercise moving along better. Another participant suggested
having a representative from CHEMTREC available to discuss how they would respond to a
hydrogen fluoride spill.
Finally, there were suggestions for following up this tabletop exercise with a functional drill.
373
06 HFEX After Action Report
HFEX - Hydrogen Fluoride Exercise
May 25, 2010
AFTER ACTION
REPORT/IMPROVEMENT PLAN
July 25, 2011
374
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375
[HFEX - Hydrogen Fluoride Exercise]
ADMINISTRATIVE HANDLING INSTRUCTIONS
1. The title of this document is "HFEX- Hydrogen Fluoride Exercise After Action
Report/Improvement Plan."
2. Point of Contact:
Exercise Director
George DiFerdinando, Jr., MD, MPH
Director
New Jersey Center for Public Health Preparedness
683 Hoes Lane West, First Floor
Piscataway, New Jersey 08854
(732)235-9039
diferdge@umdnj.edu
Handling Instructions
[Gloucester County, New Jersey]
376
[HFEX - Hydrogen Fluoride Exercise]
This page is intentionally blank.
Handling Instructions
[Gloucester County, New Jersey]
377
[HFEX - Hydrogen Fluoride Exercise]
CONTENTS
ADMINISTRATIVE HANDLING INSTRUCTIONS
CONTENTS
EXECUTIVE SUMMARY
SECTION 1: EXERCISE OVERVIEW
SECTION 2: EXERCISE DESIGN SUMMARY
SECTION 3: ANALYSIS OF CAPABILITIES
SECTION 4: CONCLUSION
APPENDIX A: PARTICIPANT FEEDBACK SUMMARY
Contents
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EXECUTIVE SUMMARY
The Gloucester County chemical release tabletop exercise, HFEX - Hydrogen Fluoride Exercise,
was developed to test Gloucester County's abilities to meet the meet the following Target
Capabilities: Emergency Operations Center (EOC) Management, Responder Safety and Health,
HazMat Response and Decontamination, Citizen Evacuation and/or Shelter-in-Place, Emergency
Triage and Pre-Hospital Treatment, Medical Surge, and Mass Care (Sheltering, Feeding, and
Related Services) capabilities. The exercise planning team was composed of numerous and
diverse agencies, including Gloucester County Department of Health and Senior Services,
Gloucester County Office of Emergency Management, Gloucester County Prosecutors Office,
New Jersey Center for Public Health Preparedness, New Jersey Department of Environmental
Protection, New Jersey Department of Health and Senior Services, New Jersey Hospital
Association, New Jersey Office of Homeland Security and Preparedness, New Jersey State
Police, and United States Department of Homeland Security.
The exercise planning team discussed the exercise goals and objectives, target capabilities to
address; which agencies and people to involve as players, facilitators, evaluators, observers, and
subject matter experts; type of scenario to use; and logistics for the exercise planning and
implementation. Some of the issues encountered during the planning process included the
scenario timeline, the number of players, and the scope of the exercise. There was discussion
during the planning of the exercise to make sure that the scenario did not become so large that it
was unmanageable and unrealistic. Another issue was whether or not the HF release was entirely
accidental and that there were no criminal/terrorist implications. During the planning, the
hospitals were concerned about their level of involvement and that it would be minimal. The
hospitals provided discussion questions for each scenario
Based on the exercise planning team's deliberations, the following objectives were developed for
HFEX:
•
Objective 1: Assess and identify how to activate and maintain emergency
communications essential to support response to an HF incident in Gloucester
County.
•
Objective 2: Demonstrate the ability to alert, mobilize, and activate personnel for
emergency response and maintain operations until the situation is brought under
control.
Objective 3: Demonstrate the ability to mobilize and track equipment, people, and
other resources in support of emergency operations.
Objective 4: Identify and implement appropriate actions to protect emergency
workers and the public.
Objective 5: Demonstrate inter-agency (Gloucester County Health Department,
hospitals, and first responders) communication and cooperation in response to an HF
incident in Gloucester County.
•
•
•
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The purpose of this report is to analyze exercise results, identify strengths to be maintained and
built upon, identify potential areas for further improvement, and support development of
corrective actions.
Major Strengths
The major strengths identified during this exercise are as follows:
•
•
The organizations and agencies cooperate well, are familiar with each other's
roles and responsibilities, and know agency representatives on a first name
basis. There were no conflicts regarding command structures and
responsibilities between players.
The first responders on the scene accurately identified the safety perimeter of
the incident site, arrived upwind, and remained outside the hot zone when
assessing the situation. Staging area was established in the safe zone and later
moved when wind direction was factored in.
Primary Areas for Improvement
Throughout the exercise, several opportunities for improvement in Gloucester County's ability to
respond to the incident were identified. The primary areas for improvement, including
recommendations, are as follows:
•
•
•
No mitigation, no offensive or defensive actions were allowed until the Gloucester
County HazMat team arrived on scene and assessed the situation. While the
responders recognized the mutual aid agreement with Valero to provide HazMat
assistance, the plan called for the Gloucester County HazMat team to arrive and
assess prior to contacting Valero for assistance. The plan had no alternate procedure
if the county HazMat team was delayed. The recommendation is to establish
alternate procedures for requesting assistance from mutual aid agreement partners as
part of a plan in case the primary authority is delayed.
There was no Gloucester County contingency plan for a hydrogen fluoride release.
The recommendation is to identify all chemicals in Gloucester County that are
extremely hazardous to the public or environment and could potentially be released in
large amounts during an emergency. Develop chemical specific contingency plans
and standard operating procedures for different types of scenarios, including worst
case scenarios. Consider including a pre-plan for a small, medium and large chemical
release to be used initially until plume modeling can be accomplished. This will
expedite the entire response effort, saving valuable time and potentially minimizing
exposures.
There was little consideration given to care and coordination of care for special needs
populations. The only sheltering option was general shelters run by Red Cross and
these shelters cannot manage medically fragile people. The only option then is to
send the special needs population to the hospitals. The recommendation is to identify
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the special needs populations within Gloucester County, so that appropriate plans are
in place to handle this population and the necessary resources. A standard operating
procedure should be drafted for special needs population care and coordination.
•
The communications between first responders and healthcare/hospitals needs to be
improved.
Overall this was a successful exercise and very well received by participants in different roles
(i.e. players, evaluators, and observers). The suggestions for areas in which future exercises
conducted by Gloucester County should focus are: issues related to special needs, such as
identification of special needs populations, transportation, and shelter locations; exercises testing
hospital surge capabilities, including space, staff, medications and other resources; regional
incident command exercises to define roles and responsibilities, especially as turnover of
positions occur; exercises focusing on EMS capabilities of mass care, including managing
patients for triage, treatment and transportation; and exercises focusing on non-management
personnel decision making in absence of management.
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SECTION
1: EXERCISE OVERVIEW
Exercise Details
Exercise Name
HFEX - Hydrogen Fluoride Exercise
Type of Exercise
Tabletop exercise
Exercise Start Date
May 25, 2010
Exercise End Date
May 25, 2010
Duration
3 hours
Location
ExxonMobil Conference Center, 600 Billingsport Road, Paulsboro, New Jersey
Sponsor
New Jersey Center for Public Health Preparedness at UMDNJ-School of Public Health
Program
U.S. Department of Defense grant awarded to the New Jersey Center for Public Health
Preparedness at UMDNJ - School of Public Health.
Mission
Response
Capabilities
• Emergency Operations Center (EOC) Management
• Responder Safety and Health
• HazMat Response and Decontamination
• Citizen Evacuation and/or Shelter-in-Place
• Emergency Triage and Pre-Hospital Treatment
• Medical Surge
• Mass Care (Sheltering, Feeding, and Related Services)
Scenario Type
Chemical release (hydrogen fluoride)
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Exercise Planning Team Leadership
Diane Anderson, Planning
New Jersey Hospital Association
Rebecca Baron, Logistics, Planning, and Operations
New Jersey Center for Public Health Preparedness
Robert Brownlee, Planning and Evaluation Team Captain
New Jersey Department of Health and Senior Services
Jack DeAngelo, Planning
Gloucester County Office of Emergency Management
George DiFerdinando, Administrative/Finance
New Jersey Center for Public Health Preparedness
William Donovan, Planning and Operations
Gloucester County Prosecutors Office
Mitchell Erickson, Planning
United States Department of Homeland Security
Bryan Everingham, Planning, Logistics and Exercise Facilitator
New Jersey State Police
Kevin Hayden, Planning
New Jersey Department of Health and Senior Services
Glenn Paulson, Planning Team Leader
New Jersey Center for Public Health Preparedness
Christine Poulsen, Logistics, Planning, and Operations
New Jersey Center for Public Health Preparedness
Dennis Quinn, Planning
New Jersey Office of Homeland Security and Preparedness
Thomas Rafferty, Planning and Logistics
New Jersey State Police
Dennis Sample, Planning and Logistics
New Jersey Office of Homeland Security and Preparedness
Robert Van Fossen, Planning, Logistics and Exercise Facilitator
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New Jersey Department of Environmental Protection
Scott Woodside, Planning
Gloucester County Department of Health and Senior Services
Participating Organizations
Atlantic Health
Burlington County Health Department
Camden County
Cooper University Hospital
Crozer-Chester Medical Center
DuPont
East Greenwich Township Fire / Rescue
ExxonMobil Tech Center
Gibbstown Fire Company
Gloucester County Emergency Medical Service
Gloucester County Department of Health and Senior Services
Gloucester County Hazardous Materials Response Team
Gloucester County Office of Emergency Management
Gloucester County Prosecutors Office
Gloucester County Chapter of the American Red Cross
Health Care Association of New Jersey
Honeywell
Kennedy Hospital
New Jersey Center for Public Health Preparedness
New Jersey Department of Environmental Protection
New Jersey Department of Health and Senior Services
New Jersey Department of Transportation
New Jersey Hospital Association
New Jersey Office of Homeland Security and Preparedness
New Jersey State Police
New Jersey Turnpike Authority Emergency Services Department
Paulsboro Emergency Management
Rowan University
Solvay Solexis, Inc.
Underwood Memorial Hospital
United States Coast Guard
United States Department of Homeland Security
Valero
West Deptford Police Department
Number of Participants
• Players: 26
• Evaluators: 8
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•
Facilitators: 2
•
Observers: 67 (including planning team)
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SECTION
2: EXERCISE DESIGN SUMMARY
Exercise Purpose and Design
The purpose of the HFEX - Hydrogen Fluoride Exercise was to provide participants with an
opportunity to evaluate their current emergency response plans and capabilities for a response to
a hydrogen fluoride release in Gloucester County. The exercise focused on key local and county
capabilities in both external and internal communication, coordination, and critical decisionmaking, and was aimed at hospital, public health, and first responder coordination of the
incident.
The HFEX exercise was developed and implemented by a planning team consisting of people
from a wide array of agencies and organizations within New Jersey, primarily within Gloucester
County. The planning process started in October, 2009 and meetings with the planning team
were held at minimum monthly. The exercise was designed using the Homeland Security
Exercise and Evaluation Program, and was organized as a facilitated tabletop exercise
accompanied by a multimedia presentation. At different points in the exercise there were injects
and updates which summarized key scenario events occurring within that time period. After the
updates, participants engaged in a facilitated group discussion of appropriate response issues and
subsequent actions based on the scenario. The exercise was funded by a grant from the U.S.
Department of Defense awarded to the New Jersey Center for Public Health Preparedness at
UMDNJ - School of Public Health.
Exercise Objectives, Capabilities, and Activities
Capabilities-based planning allows for exercise planning teams to develop exercise objectives
and observe exercise outcomes through a framework of specific action items that were derived
from the Target Capabilities List (TCL). The capabilities listed below form the foundation for
the organization of all objectives and observations in this exercise. Additionally, each capability
is linked to several corresponding activities and tasks to provide additional detail.
Based upon the identified exercise objectives below, the exercise planning team has decided to
demonstrate the following capabilities during this exercise:
•
Objective 1: Assess and identify how to activate and maintain emergency
communications essential to support response to an HF incident in Gloucester
County.
• Emergency Operations Center (EOC) Management: Activity 1: Activate
EOC/MACC/IOF; Activity 2: Direct EOC/MACC/IOF Tactical Operations;
Activity 3: Gather and Provide Information; Activity 4: Identify and Address
Issues; Activity 5: Prioritize and Provide Resources; Activity 6: Provide
EOC/MACC/IOF Connectivity; Activity 7: Support and Coordinate
Response; and Activity 8: Demobilize EOC/MACC/IOF Management.
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•
Objective 2: Demonstrate the ability to alert, mobilize, and activate personnel for
emergency response and maintain operations until the situation is brought under
control.
- Emergency Operations Center (EOC) Management: Activity 1: Activate
EOC/MACC/IOF; Activity 2: Direct EOC/MACC/IOF Tactical Operations;
Activity 3: Gather and Provide Information; Activity 4: Identify and Address
Issues; Activity 5: Prioritize and Provide Resources; Activity 6: Provide
EOC/MACC/IOF Connectivity; Activity 7: Support and Coordinate
Response; and Activity 8: Demobilize EOC/MACC/IOF Management.
- I la/ Mat Response and Decontamination: Activity 1: Site Management and
Control; Activity 2: Identify the Problem; Activity 3: Hazard Assessment and
Risk Evaluation; Activity 4: Information Management and resource
Coordination; Activity 5: Implement Response Objectives; and Activity 6:
Decontamination and Clean-Up/Recovery Operations.
- Emergency Triage and Pre-Hospital Treatment: Activity 1: Direct Triage
and Pre-Hospital Treatment Tactical Operations; Activity 2: Activate Triage
and Pre-Hospital Treatment; and Activity 5: Transport.
•
Objective 3: Demonstrate the ability to mobilize and track equipment, people, and
other resources in support of emergency operations.
• Emergency Operations Center (EOC) Management: Activity 1: Activate
EOC/MACC/IOF; Activity 2: Direct EOC/MACC/IOF Tactical Operations;
Activity 3: Gather and Provide Information; Activity 4: Identify and Address
Issues; Activity 5: Prioritize and Provide Resources; Activity 6: Provide
EOC/MACC/IOF Connectivity; Activity 7: Support and Coordinate
Response; and Activity 8: Demobilize EOC/MACC/IOF Management.
• Ha/Mat Response and Decontamination: Activity 1: Site Management and
Control; Activity 2: Identify the Problem; Activity 3: Hazard Assessment and
Risk Evaluation; Activity 4: Information Management and resource
Coordination; Activity 5: Implement Response Objectives; and Activity 6:
Decontamination and Clean-Up/Recovery Operations.
• Emergency Triage and Pre-Hospital Treatment: Activity 1: Direct Triage
and Pre-Hospital Treatment Tactical Operations; Activity 2: Activate Triage
and Pre-Hospital Treatment; and Activity 5: Transport.
- Citizen Evacuation and/or Shelter-in-Place: Activity 1: Direct Evacuation
and/or In-Place Protection Tactical Operation; and Activity 2: Activate
Evacuation and/or In-Place Protection.
- Medical Surge: Activity 1: Pre-event Mitigation and Preparedness; Activity
2: Incident Management; Activity 3: Increase Bed Surge Capacity; Activity 4:
Medical Surge Staffing Procedures; Activity 5: Decontamination; Activity 6:
Receive, Evaluate, and Treat Surge Casualties; Activity 7: Provide Surge
Capacity for Behavioral Health Issues; and Activity 8: Demobilize.
- Mass Care (Sheltering, Feeding, and Related Services): Activity 1: Direct
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Mass Care Tactical Operations; Activity 2: Activate Mass Care; and Activity
3: Shelter Special Needs.
Objective 4: Identify and implement appropriate actions to protect emergency
workers and the public.
- Responder Safety and Health: Activity 1: Activate Responder Safety and
Health; Activity 2: Identify Safety/PPE Needs and Distribute PPE; and
Activity 3: Site/Incident Specific Safety and Health Training.
- HazMat Response and Decontamination: Activity 1: Site Management and
Control; Activity 2: Identify the Problem; Activity 3: Hazard Assessment and
Risk Evaluation; Activity 4: Information Management and resource
Coordination; Activity 5: Implement Response Objectives; and Activity 6:
Decontamination and Clean-Up/Recovery Operations.
- Citizen Evacuation and/or Shelter-in-Place: Activity 1: Direct Evacuation
and/or In-Place Protection Tactical Operation; and Activity 2: Activate
Evacuation and/or In-Place Protection.
- Emergency Triage and Pre-Hospital Treatment: Activity 1: Direct Triage
and Pre-Hospital Treatment Tactical Operations; Activity 2: Activate Triage
and Pre-Hospital Treatment; and Activity 5: Transport.
- Mass Care (Sheltering, Feeding, and Related Services): Activity 1: Direct
Mass Care Tactical Operations; Activity 2: Activate Mass Care; and Activity
3: Shelter Special Needs.
Objective 5: Demonstrate inter-agency (Gloucester County Health Department,
hospitals, and first responders) communication and cooperation in response to an HF
incident in Gloucester County.
• Emergency Operations Center (EOC) Management: Activity 1: Activate
EOC/MACC/IOF; Activity 2: Direct EOC/MACC/IOF Tactical Operations;
Activity 3: Gather and Provide Information; Activity 4: Identify and Address
Issues; Activity 5: Prioritize and Provide Resources; Activity 6: Provide
EOC/MACC/IOF Connectivity; Activity 7: Support and Coordinate
Response; and Activity 8: Demobilize EOC/MACC/IOF Management.
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Scenario Summary
The scenario for the exercise was a transportation incident involving a tank truck carrying
hydrogen fluoride. No specific Gloucester County industrial facility was named or implicated in
the exercise. The scenario was split into three parts. In part 1, a tank truck with a UN 1052 label
and placards indicating corrosive and poison exits Interstate 295 northbound at exit 18 in East
Greenwich, NJ. The truck swerves and overturns on the exit ramp next to a truck stop busy with
truckers and other patrons. The contents of the tank truck are starting to leak. In part 2, the
contents leaking are staring to move with the prevailing winds and people down wind are
experiencing symptoms. In part 3, indoor and outdoor concentrations of HF at the site of the
incident reached dangerous levels and calls are coming in from areas farther down wind
reporting symptoms of HF exposure.
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SECTION
3: ANALYSIS OF CAPABILITIES
This section of the report reviews the performance of the exercised capabilities, activities, and
tasks. In this section, observations are organized by capability and associated activities. The
capabilities linked to the exercise objectives of HFEX - Hydrogen Fluoride Exercise are listed
below, followed by corresponding activities. Each activity is followed by related observations,
which include references, analysis, and recommendations.
Capability 2: Responder Safety and Health
Capability Summary: Responder Safety and Health is the capability that ensures adequate
trained and equipped personnel and resources are available at the time of an incident to protect
the safety and health of on-scene first responders, hospital/medical facility personnel (first
receivers), skilled support personnel, and, if necessary, their families through the creation and
maintenance of an effective safety and health program. This program needs to comply with the
Occupational Safety and Health Administration (OSHA) and any other applicable Federal and
State regulations and health and safety standards.
Responder safety and health was not fully addressed in this exercise. No safety officer was
named and as a result no safety and health plan was developed, on-going health and safety
assessments were not occurring and site resource needs and training was not conducted. First
responders correctly identified a place up-wind from the incident at which to place the incident
command post, however, the chemical was not identified and mitigation was continuously
delayed until the county HazMat team arrived on scene with proper PPE, which was 1-1.5 hours
into the event.
Observation 1: Strength: First responders recognized the dangers of approaching the spill.
References: None
Analysis: Early arriving responders (police, EMS and fire) appropriately arrived from
an upwind route, remained out of the hot zone when assessing the situation and during
the identification of the contents of the overturned tanker. The two care accident victims
still in the immediate area of the spill created an incentive to enter and rescue, but the
responders recognized they were not appropriately protected.
Recommendations: None
Observation 2: Strength: First responders were aware of a mutual aid agreement with
Valero.
References: None
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Analysis: Responders recognized that Valero had experience and resources and a
mutual aid agreement to provide assistance if called for local incidents in keeping with
the plan.
Recommendations: None
Observation 3: Area of Improvement: The Valero mutual aid agreement required the
County HazMat team to be on scene and assess situation prior to providing their assistance.
References: None
Analysis: Valero was not contacted because the County HazMat team did not arrive
until an hour and a half into the incident. There was no alternate procedure discussed.
Recommendations: Establish alternate procedures for requesting assistance from
mutual aid agreement partners as part of the plan, in case the primary authority is
delayed.
Observation 4: Area of Improvement: The incident command post location was first set up
down-wind, later moved upwind.
References: None
Analysis: Greenwich Police Department, the first on the scene and original incident
command, selected the Travel Center south west of the incident as the incident command
post location. They seemed aware of the wind location, but intent on other qualities of
the location, such as easy reach. They were quick to change location up-wind but were
still too close to the incident (<700 feet). The police department mentioned the
emergency response guide for hydrogen fluoride, but did not show the requisite use of the
guidance.
Recommendations: Identify the primary location of the incident command post and
staging area to be up-wind from the incident at the distance defined in the emergency
response guide for HF and include this distance in the contingency plan. Stress this issue
in training of first responders and incident commanders.
Observation 5: Area of Improvement: The incident commander did not designate a safety
officer.
References: None
Analysis: The incident commander is acting safety officer until one is designated. At
the beginning, nobody with appropriate credentials was on site. However, with a HazMat
incident the IC should recognize the immediate need for a safety officer who can focus
on safety planning early in the incident. While there seemed to be some question of
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when the fire departments would arrive on scene, a fire chief should be on scene
relatively quickly and capable of acting as a temporary safety officer.
Recommendations: Modify the emergency response plan and appropriate procedures
to implement immediate evacuation/shelter-in-place warnings in keeping with the
emergency response guide.
Observation 6: Area of Improvement: No mitigation, offensive or defensive actions were
allowed until the HazMat team arrived on scene and assessed the situation.
References: None
Analysis: Waiting for HazMat even to begin the site assessment was too much of a
delay in mitigation, especially considering the Valero HazMat team was five minutes
away. Even if Valero cannot provide HazMat team for offensive mitigation operations,
they may be able to supply expertise off-site, equipment, and perhaps perform plume
modeling.
Recommendations: Identify local expertise in the emergency plan who can be
contacted. Establish a procedure in which a primary authority that can activate mutual
aid agreements can be in contact with people on site and thus assess the scene or establish
alternate procedures for activating mutual aid agreements, not depending on arrival of
HazMat team.
Observation 7: Area of Improvement: Create pre-plans for extremely hazardous materials.
References: None
Analysis: There was no Gloucester County contingency plan for a hydrogen fluoride
release. Pre-planning for a hazardous materials release incident will allow responders to
more quickly and safely deploy, plan and mitigate the incident. The Right to Know
Standard mandates the collection of hazardous materials information at the local police
and fire departments, as well as the local emergency planning committee at the county
level. RTK information can be used to identify chemicals present in the community for
which pre-plans should be developed.
Recommendations: The recommendation is to identify all chemicals in Gloucester
County that are extremely hazardous to the public or environment and could potentially
be released in large amounts during an emergency. Develop chemical specific
contingency plans and standard operating procedures for different types of scenarios,
including worst case scenarios.
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Capability 3: HazMat Response and Decontamination
Capability Summary: Hazardous Materials Response and Decontamination is the capability
to assess and manage the consequences of a hazardous materials release, either accidental or as
part of a terrorist attack. It includes testing and identifying all likely hazardous substances onsite;
ensuring that responders have protective clothing and equipment; conducting rescue operations
to remove affected victims from the hazardous environment; conducting geographical survey
searches of suspected sources or contamination spreads and establishing isolation perimeters;
mitigating the effects of hazardous materials, decontaminating on-site victims, responders, and
equipment; coordinating off-site decontamination with relevant agencies, and notifying
environmental, health, and law enforcement agencies having jurisdiction for the incident to begin
implementation of their standard evidence collection and investigation procedures.
HazMat response and decontamination was fully addressed in this exercise. Issues involving site
management and control, hazard assessment and mitigation were discussed. The HazMat team
was integrated into the unified command, they established hot and support zones and completed
a full site assessment upon arrival. The HazMat priorities were verbalized, however, no incident
action plan was developed and the HazMat team did not utilize or reference written plans during
the exercise.
Observation 1: Strength: Early HazMat intervention and establishment of unified
command.
References: l. The 911 tele-communicators have HazMat training and can prompt for
response. 2. The HazMat resources were incorporated into the incident command system
fully and immediately upon arrival on scene.
Analysis: Based upon the 911 tele-communicators and the quick size up by the Fire
Department, the HazMat assessment team was dispatched quickly.
Recommendations: None
Observation 2: Area of improvement: Develop pre-plans for incidents of this nature.
References: None
Analysis: SOPs/SOGs should not only be developed for the routine events and
responses, but should also be considered for some worst case scenarios.
Recommendations: Develop SOP/SOG for a response to HF incidents - this will
expedite the entire response effort, saving valuable time and possibly minimizing
exposures. Consider preplanning a small, medium and large vapor release to be used
initially until modeling can be accomplished.
Observation 3: Area of improvement: First aid training for hydrogen fluoride exposure.
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References: None
Analysis: Hydrogen fluoride toxicology was discussed and while there was a consensus
that a 5-minute wash is appropriate there seemed to be a lack of immediate follow-up
treatment.
Recommendations: Examine how the follow-up treatment would be administered, in
a timely manner, especially when dealing with mass exposures. Identify any
impediments to this treatment and formulate solutions.
Capability 4: Citizen Evacuation and/or Shelter-in-Place
Capability Summary: Citizen evacuation and shelter-in-place is the capability to prepare for,
ensure communication of, and immediately execute the safe and effective sheltering-in-place of
an at-risk population (and companion animals), and/or the organized and managed evacuation of
the at-risk population (and companion animals) to areas of safe refuge in response to a
potentially or actually dangerous environment. In addition, this capability involves the safe
reentry of the population where feasible.
We did not complete this capability because we ran out of time during the exercise.
Capability 5: Emergency Triage and Pre-Hospital Treatment
Capability Summary: Triage and Pre-Hospital Treatment is the capability to appropriately
dispatch emergency medical services (EMS) resources; to provide feasible, suitable, and
medically acceptable pre-hospital triage and treatment of patients; to provide transport as well as
medical care en-route to an appropriate receiving facility; and to track patients to a treatment
facility.
Emergency Triage and Pre-Hospital Treatment was not fully addressed in this exercise. Issues
involving activation and direction of triage and pre-hospital treatment were discussed, but the
actual triage, treatment and transport of patients was not emphasized because the exercise
scenario did not allow for it. This exercise involved more the public safety and pre-hospital
response and not the treatment of patients.
Observation 1: Strength: Interagency Communications worked well.
References: Procedural
Analysis: Noted EMS and fire could communicate directly via radio and indirectly with
law enforcement through shared dispatch. Noted inclusion of basic life support and
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advanced life support supervisors to aid communications with personnel, unified
command and the hospitals.
Recommendations: Assure that non-management personnel are aware of these
communication procedures, in case management is not immediately available.
Observation 2: Strength: EMS and HazMat used proper safety precautions.
References: Procedural
Analysis: EMS was very firm in position of setting up and awaiting access to patients
after decontamination.
Recommendations: Assure that non-management personnel are equally aware of
safety procedures in this type of incident.
Observation 3: Strength: EMS demonstrated cooperation.
References: Procedural
Analysis: The EMS chief clearly demonstrated assessing the need for backfill of other
EMS units (county wide system) either using off duty staff or out of county strike team.
Recommendations: Assure that supervisors would take similar actions that as the
chief.
Observation 4: Area of Improvement: Exercise did not appropriately test EMS capabilities
with mass care.
References: None
Analysis: Exercise did not fully expand and thus EMS was not challenged with
managing patients for triage, treatment and transportation.
Recommendations: Consider this a component for a future exercise.
Observation 5: Area of Improvement: Exercise did not appropriately test hospital surge
capabilities and management of impact on facility.
References: It was noted during the exercise that the incident management team would
be alerted/activated, but without the patients it was never truly assessed.
Analysis: Exercise did not fully expand and thus the hospitals were not challenged for
surge capacity and impact on facility.
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Recommendations: Consider this a component for a future exercise.
Capability 6: Medical Surge
Capability Summary: Medical Surge is the capability to rapidly expand the capacity of the
existing healthcare system (long-term care facilities, community health agencies, acute care
facilities, alternate care facilities and public health departments) in order to provide triage and
subsequent medical care. This includes providing definitive care to individuals at the appropriate
clinical level of care, within sufficient time to achieve recovery and minimize medical
complications. The capability applies to an event resulting in a number or type of patients that
overwhelm the day-to-day acute-care medical capacity. Medical Surge is defined as the rapid
expansion of the capacity of the existing healthcare system in response to an event that results in
increased need of personnel (clinical and non-clinical), support functions (laboratories and
radiological), physical space (beds, alternate care facilities) and logistical support (clinical and
non-clinical equipment and supplies).
Medical surge was not fully addressed in this exercise. Issues involving hospital
decontamination capabilities and triage were discussed, but the transport of patients from the
field to the hospital was not emphasized. This exercise involved more the public safety and prehospital response and not the medical treatment of patients.
Observation 1: Strength: Incident command was appropriately activated.
References: NIMS/ICS
Analysis: Appropriate activation of incident command with upgrade to unified
command. Lead agencies were quickly identified. Organizations were familiar with
partners and stakeholders, even knowing each other in a first name basis.
Recommendations: Continue strengthening partnerships through discussions, projects
and exercises.
Observation 2: Strength: Staging area was appropriately located.
References: Decon/HazMat
Analysis: Staging area was established based on hazards and later moved when wind
direction was factored in.
Recommendations: Continue strengthening partnerships through discussions, projects
and exercises.
Section 3: Analysis of Capabilities
[Gloucester County, New Jersey]
397
[HFEX - Hydrogen Fluoride Exercise]
Observation 3: Strength: Partnerships across organizations and agencies.
References: NIMS/ICS
Analysis: Organizations and agencies have worked together in the past and are familiar
with roles and responsibilities. There were no conflicts regarding who is in charge and
what each agency is supposed to do.
Recommendations: Continue to exercise and define roles and responsibilities,
especially when turnover of positions occur.
Observation 4: Area of improvement: Special population needs were not fully considered.
References: Cashes of equipment to set up 25-50 beds for special needs sheltering have
been purchased through regional grant funds, but a standard operating procedure has not
been developed to address operations and logistics (location, staffing, liability coverage,
evacuation criteria, etc.).
Analysis: There was a little consideration for the special needs population. With the
plan to evacuate neighborhoods due to the chemical exposure, the only sheltering option
is the general shelters run by Red Cross. Since these shelters cannot manage medically
fragile people, there are no other options other than sending the special needs populations
to the hospitals.
Recommendations: The special needs population needs to be identified so that
appropriate resources are available. Default plan is to send this population to the
hospitals. A standard operating procedure needs to be drafted, including major
stakeholders. The upcoming regional exercise involving special needs populations will
help illustrate the importance of this issue and vet problem through leadership.
Observation 5: Area of improvement: Medical surge was not fully tested in this exercise.
References: Hospital disaster plan, county emergency management plans.
Analysis: Medical surge at the hospitals was not tested to stress current plans and
resources. Mass documentation and triage were not specifically addressed during the
exercise.
Recommendations: An exercise to test hospital surge capabilities, including space,
staff, medications, and other resources.
Observation 6: Area of improvement: Not all organizations were familiar with
communications.
Section 3: Analysis of Capabilities
[Gloucester County, New Jersey]
398
[HFEX - Hydrogen Fluoride Exercise]
References: County communications plans.
Analysis: Some organizations were not familiar with the communication channels used
by partners. Not all communication modalities interface together. A major issue was
communication between EMS and hospitals, which appears to be separate from other
modalities used by first responders.
Recommendations: Identify one point of contact for each agency, responsible for
receiving information and sharing information to other partners. Create redundancy and
test for interoperability.
Capability 7: Mass Care (Sheltering, Feeding and Related Services)
Capability Summary: Mass Care is the capability to provide immediate shelter, feeding
centers, basic first aid, bulk distribution of needed items, and related services to persons affected
by a large-scale incident, including special needs populations. Special needs populations include
individuals with physical or mental disabilities who require medical attention or personal care
beyond basic first aid. Other special-needs populations include non-English speaking populations
that may need to have information presented in other languages. The mass care capability also
provides for pet care/handling through local government and appropriate animal-related
organizations. Mass care is usually performed by nongovernmental organizations (NGOs), such
as the American Red Cross, or by local government-sponsored volunteer efforts, such as Citizen
Corps. Special-needs populations are generally the responsibility of local government, with
medical needs addressed by the medical community and/or its alternate care facilities. State and
Federal entities also play a role in public and environmental health by ensuring safe conditions,
safe food, potable water, sanitation, clean air, etc.
Mass care was not fully addressed in this exercise. While the mass care plan was activated and
the services coordinated for the general populations, the mass care for special needs populations
was not fully addressed.
Observation 1: Strength: Developed mass care plan and identified shelter sites.
References: None
Analysis: Plan was noted and shelter sites identified. , but needs revision given time
and day of event, with respect to identified shelter.
Recommendations: Consideration should be given to coordination with a shelter
provider, transportation needs, care of elderly and special needs populations.
Observation 2: Area of improvement: unclear coordination with school officials for sites
of shelter.
References: None
Section 3: Analysis of Capabilities
[Gloucester County, New Jersey]
399
[HFEX - Hydrogen Fluoride Exercise]
Analysis: There was confusion concerning coordination with the school officials for
shelter locations and consideration of on-going school activities.
Recommendations: Consider additional locations for sheltering and create contact
ahead of time with persons in charge of these facilities.
Observation 3: Area of improvement: Transportation for mass care coordination was not
fully planned out.
References: None
Analysis: Transport requirements for special needs and the elderly population was not
fully addressed. Also, transportation of shelter supplies to sites was not discussed.
Recommendations: Identify special needs population within the county and develop
special transportation services.
Section 3: Analysis of Capabilities
[Gloucester County, New Jersey]
400
[HFEX - Hydrogen Fluoride Exercise]
SECTION
4: CONCLUSION
The Gloucester County chemical release tabletop exercise, HFEX - Hydrogen Fluoride Exercise,
was held on May 25, 2010. HFEX was developed to test Gloucester County's abilities to meet
seven Target Capabilities. Participants exercised with the scenario of an accidental HF release
from an overturned tanker in Gloucester County.
The major strengths of the exercise, based on hot wash and evaluation comments, included the
organizations' and agencies' ability to cooperate; the participants' knowledge of command
structures and of other agencies; and the setting up of the staging area and the identification of
the hot zone.
Areas of improvement were also identified. Those areas were a delay in mitigation or
offensive/defensive actions at the scene; no county contingency plan for a hydrogen fluoride
release; no consideration for special needs populations; and a lack of communication between
the hospitals and the first responders.
Recommendations from this exercise are the establishment of alternate procedures for requesting
mutual aid for HazMat events; the development of a county contingency plan for a chemical
release of different sizes; the creation of a standard operating procedure for special needs
populations; and the improvement of communication methods between first responders and
hospitals.
This report should be used by Gloucester County for corrective actions.
Section 4: Conclusion
401
[HFEX - Hydrogen Fluoride Exercise]
APPENDIX A: PARTICIPANT FEEDBACK SUMMARY
HFEX- HYDROGEN FLUORIDE EXERCISE PARTICIPANT FEEDBACK FORM
SUMMARY
"HFEX- Hydrogen Fluoride Exercise" was held on May 24, 2010. The exercise was held at the
Exxon-Mobil Technology Center in Paulsboro, NJ. There were a total of 101 participants and 40
(39.6%) completed participant feedback forms. Of the participants who completed the forms; 3
(8%) were evaluators, 18 (45%) were observers, 17 (43%) were players, and 2 (5%) were subject
matter experts. The participants who completed the feedback forms were from a wide range of
agencies that were at the exercise. Although it was optional, 27 of the participants wrote in their
names and agencies.
The participant feedback form was altered from the Homeland Security Exercise and Evaluation
Program (HSEEP) participant feedback form. The form had three sections; Part I:
Recommendations and Corrective Actions, Part II: Assessment of Training Session and Exercise
Design and Conduct, and Part III: Participant Feedback. The questions in Part II were written
using a Likert scale of 1 to 5, with 5 being the highest value.
The first question in Part I asked the top strengths of the exercise. Communications, interagency
cooperation, use of ICS and unified command were the top three strengths that were listed.
Participants also noted the brainstorming of ideas, the participants were varied and
representative, a comfortable exercise environment, the players had strong knowledge of their
roles and responsibility, and a realistic scenario.
The second question in Part I asked the top three areas that need improvement.
The offensive actions of the HazMat team in controlling the spill, chain of command, and
communications between agencies could be improved. Other comments including better
communication with the hospitals, treatment of causalities, more discussion on how to protect
first responders, and how to handle special needs populations.
The third question in Part I asked, " Identify the action steps that should be taken to address the
issues you identified above. For each action step, indicate if it is a high, medium, or low
priority." The corrective actions identified were:
High Priority
County and local entities
need to be able to
communicate directly.
Mitigation of the vapor/leak
Accuracy of the
Medium Priority
Expect that bystanders will
be involved, call 9-1-1, and
become victims themselves,
and work the best solutions
based on that more realistic
model.
Evaluate ability to save lives
first before logistical and
jurisdictional issues.
Understanding scale of
402
Low Priority
Fatality management.
HFEX- Hydrogen Fluoride Exercise
notifications.
incident
Good understanding of task
forces plus resource needs
Realize that initial units
maybe affected and become
part of the problem
Recovery attempts
Working of multiple PIO's
together
Identify benefits and
concerns upon evacuations of
schools and senior housing
areas and other special needs
populations.
The fourth question in Part I asked, "List the policies, plans, and procedures that should be
reviewed, revised, or developed. For each, indicate if it is a high, medium, or low priority." The
items for review identified were:
High Priority
Evaluate behavioral science
research and establish what
people are likely to do and
incorporate it into conops
(continuing operations)
Re-evaluate communication
policies across counties and
agencies
Re-evaluate priorities on the
incident and make sure the
order of operations and
urgency of patient care is
realistic
All SOPs should be
reviewed.
Preplan for extreme hazard
chemicals found in
jurisdiction, train all PD and
FD to id chemical as first
step.
Contacting immediate area
(truck stop) and informing
them to evacuate due to
hazardous material spill, save
lives.
Offensive action to minimize
incident
Medium Priority
Overturned tanker (HazMat
suspected) protocol.
Department of Health's' role
in similar situation, better
identified
Establishing a common
meeting place for agency
representatives.
Plume modeling for several
other scenarios
Fatality management- ME,
LE, and responder
partnership
Realistic plan to coordinate
required evacuations with
offensive actions that can be
taken.
403
Low Priority
ARC needs from local
municipalities, LE, EMS
HFEX- Hydrogen Fluoride Exercise
Special needs and shelter
operations
Part II of the form was Assessment of Training Session and Exercise Design and Conduct.
Participants rated their overall assessment of the exercise on a scale from 1 to 5, with 1
indicating strong disagreement with the statement, 5 indicating strong agreement, and 7
indicating don't know or not applicable. All of the means for the assessment factors were greater
than 4, with the highest being a 4.68. The assessment factor with the greatest average and most
number of 5's was on the trainers. The weakest of the factors was on the Situation Manual and
its use during the exercise.
Strongly Disagree ->
Strongly Agree
Assessment Factor
Total
1
2
3
4
5
Mean
The morning training session
prepared the participants for the
tabletop exercise and discussion.
N
%
0
0.0
1
0.03
4
0.10
14
0.35
21
0.53
40
1.00
4.38
The trainers were knowledgeable on
the topic and their presentations were
understandable.
N
%
0
0.0
1
0.03
1
0.03
8
0.20
30
0.75
40
1.00
4.68
The exercise was well structured and
organized.
N
%
0
0.0
0
0.0
3
0.08
16
0.40
21
0.53
40
1.00
4.45
The exercise scenario was plausible
and realistic.
N
%
0
0.0
1
0.03
4
0.10
18
0.45
17
0.43
40
1.00
4.28
The multimedia presentation helped
the participants understand and
become engaged in the scenario.
N
%
0
0.0
2
0.05
3
0.08
18
0.45
17
0.43
40
1.00
4.28
The facilitators were knowledgeable
about the material, kept the exercise
on target, and were sensitive to group
dynamics.
N
%
0
0.0
0
0.0
2
0.05
19
0.48
19
0.48
40
1.00
4.43
The Situation Manual used during the
exercise was a valuable tool
throughout the exercise.
N
%
0
0.0
0
0.0
11
0.29
14
0.37
13
0.34
40
1.00
4.05
Participation in the exercise was
appropriate for someone in my
position.
N
%
0
0.0
0
0.0
2
0.05
15
0.38
23
0.58
40
1.00
4.53
The participants included the right
people in terms of level and mix of
disciplines.
N
%
0
0.0
0
0.0
0
0.0
14
0.35
26
0.65
40
1.00
4.65
In part III - Participant Feedback of the exercise evaluation the participants were asked what
404
HFEX- Hydrogen Fluoride Exercise
changes they would make to the overall exercise and to provide recommendations for how to
improve future exercises.
Overall the exercise was well received. Some of the positive comments on the exercise were that
it was well planned, well organized, and well done; the facilitators did a good job moving along
the scenario; the facility was good; and that it was a great opportunity for multiple agencies to
collaborate.
Several participants made recommendations for how to change and enhance future exercises.
Some people stated that the scenario and timeline could have more realistically reflected the
numbers of victims and response timelines. One person felt that the numbers of victims were
underestimated due to the fact that the exercise operated on what the people should do in such a
situation and not what they will do. Another person felt that it is important to push the tabletop
participants to be realistic in their time frames for response and completion of tasks.
Another area where participants recommended changes the focus of the exercise. Some
participants recommended focusing more on mitigation and medical care/treatment and less on
the HazMat response. Other suggestions were to discuss more in depth hot zone rescue, level A
entry and shelter options. One participant suggested that if the tabletop content had been
diminished there would have been more time to cover other areas. Another suggestion was to
make the exercise in two phases, one like HFEX at 0-60 min and a second one at 12 or 24 hours
to evaluate fatigue, shift change, etc. Finally, a participant stated that the facilitators need to
control the focus of the exercise as some players by nature were more engaged and dominated
the exercise with their specific concerns and responsibilities.
The exercise was accompanied by a PowerPoint presentation, but some participants
recommended further exercise aides, such as: A map in the SitMan similar in scale to the map
used in the PowerPoint presentation, access to the MSDS, Radio transmissions to support injects,
a scribe taking notes for everybody to more easily follow the scenario and activities and
responsibilities. One participant recommended that a brief instruction on how tabletop exercises
proceed prior to start would get the exercise moving along better. Another participant suggested
having a representative from CHEMTREC available to discuss how they would respond to a
hydrogen fluoride spill.
Finally, there were suggestions for following up this tabletop exercise with a functional drill.
405
07 HFEX Steering Committee Members
Hydrogen Fluoride Emergency Response Exercises
Steering Committer Members
Rebecca Baron
New Jersey Center for Public Health Preparedness
Phone:(732)235-9094
Email: baronre@umdnj.edu
Robert Brownlee
New Jersey Department of Health and Senior Services
Phone: (609) 292-2525
Email: robert.brownlee@doh.state.nj.us
Jack DeAngelo
Gloucester County OEM
Phone:(856)307-7100
Email: jdeangelo@co.gloucester.nj.us
George DiFerdinando
New Jersey Center for Public Health Preparedness
Phone:(732)235-9039
Email: diferdge@umdnj.edu
Bill Donovan
Gloucester County Prosecutor's Office - Homeland Security
Phone: (856) 384-5606
Email: wdonovan@co.gloucester.nj.us
Mitchell Erickson
US Department of Homeland Security - Science and Technology
Phone:(202)255-2312
Email: mitchell.erickson@dhs.gov
Bryan Everingham
New Jersey State Police/Office of Emergency Management
Phone: (609) 561-1800 ext. 3346
Email: lpp5347@gw.njsp.org
Kevin Hayden
New Jersey Department of Health and Senior Services
Phone: (609) 984-5647
Email: kevin.hayden@doh.state.nj.us
Glenn Paulson
New Jersey Center for Public Health Preparedness
406
Phone:(732)235-9773
Email: paulsogl@umdnj.edu
Joe Picciano
New Jersey Office of Homeland Security and Preparedness
Phone: (609) 902-8658
Email: joseph.picciano@ohsp.state.nj.us
Christine Poulsen
New Jersey Center for Public Health Preparedness
Phone:(732)235-9612
Email: poulsemb@umdnj.edu
Dennis Quinn
New Jersey Office of Homeland Security and Preparedness
Phone: (609) 584-4346
Email: dennis.quinn@ohsp.state.nj.us
Dennis A. Sample
New Jersey Office of Homeland Security and Preparedness
Phone:609-631-7681
Email: dennis.sample@ohsp.state.nj.us
Valerie Sellers
New Jersey Hospital Association
Phone:(609)275-4261
Email: vsellers@NJHA.com
407
PLAYERS - HFEX
Response
Organization
Attending
Not attend ir
Attending
Attending
East Greenwich Twp Fire
Gibbstown Fire
Gibbstown Police Department
Paulsboro Fire
West Deptford Fire
Attending
Paulsboro OEM/Fire
Attending
Paulsboro OEM/Fire
• East Greenwich Twp OEM
Attending
Greenwich Township OEM
• Gloucester County OEM
Attending
Gloucester County OEM
Attending
East Greenwich Twp Police
Notattendin Greenwich Township Police Department
Greenwich Township Police Department
Attending
Attending
Greenwich Township Police Department
Greenwich Township Police Department
Attending
Attending
Greenwich Township Police Department
Attending
Paulsboro Police
Attending
West Deptford Police
Attending
West Deptford Police
Attending
NJSP, Incident Management Unit
NJ DOT
Attending
Attending
NJSP
Attending
NJ Turnpike Authority Emergency Services Dept
Attending
NJ Turnpike Authority Emergency Services Dept
Attending
Gloucester County EMS
No reply
Gloucester County EMS
Attending
Underwood Memorial Hospital
Attending
Underwood Memorial Hospital
Attending
Kennedy Health System
Attending
Kennedy University Hospital
Cooper University Hospital
Attending
Gloucester County HD
Attending
Attending
Gloucester County HD - Environmental
Notattendin Gloucester County HD
Attending
Gloucester County HAZMAT
Attending
Gloucester County HAZMAT
Attending
Gloucester County HAZMAT
Attending
American Red Cross, Gloucester County Chapter
NofatflSain Gloucester County ERT
31 TOTAL
408
09 RSS Facilitator's Situation Manual
Receipt, Stage, and
Storage (RSS) Warehouse
Operations:
A Tabletop Exercise
Camden County
April 14,2011
The New Jersey
^^
NJCPHPf
t^
Center for Public Heoltll
409
|red
Redness at UMDNJ
Table of Contents
Part I- Introduction
Purpose
3
Scope
3
Exercise Objectives
3
Participants
3
Exercise Agenda
3
Exercise Guidelines
3
Assumptions and Artificialities
4
Part II- Scenario
Day 1
5
Day 2
6
Day 3
7
Day 5
8
Part III-Appendix
RSS Warehouse Operations Flow Diagram: Pre-RSS Operations
9
RSS Warehouse Operations Flow Diagram: Primary Operations for RSS
10
RSS Warehouse Operations Flow Diagram: Post RSS Operations
11
Anthrax Fact Sheet
12
410
Part I- Introduction
Purpose:
The purpose of the Camden County Receipt, Stage, and Storage (RSS) Warehouse
Operations: A Tabletop Exercise is to coordinate RSS operations in Camden County.
The exercise will test the capabilities of current plans, policies, and procedures related to
RSS.
Scope:
The scope of this exercise will focus on the various emergency responders' roles in
response to a bioterror attack and the stages of RSS operations. More important than the
minute details are the processes and decision-making. The emphasis should be on
coordination, integration, problem identification, and problem resolution.
Exercise Objectives:
1. To identify shortfalls in resources, limits in capabilities, and gaps in planning and
coordination for RSS.
2. To exercise the local decision-making process and identify areas needing
refinements.
3. Review and list the various roles, functions, and procedures involved in RSS
activation
4. Identify issues relevant to RSS activation and preparing for mass dispensing, e.g.,
policies, resources, communication, coordination, and data management.
Participants:
•
•
•
•
Players
Facilitator
Evaluator
Observers
Exercise Agenda:
9:30 - 10:00
10:00-11:40
11:40-12:00
12:00
Welcome and Overview
Exercise
Hot Wash
Adjourn
Exercise Guidelines:
This TTX will be held in an open, low-stress, no-fault environment. Varying viewpoints,
even disagreements, are expected. Respond on the basis of your knowledge of current
411
plans and capabilities (i.e., you may use only existing assets) and insights derived from
your training.
Decisions are not precedent setting and may not reflect your organization's final position
on a given issue. This exercise is an opportunity to discuss and present multiple options
and possible solutions. Issue identification is not as valuable as suggestions and
recommended actions that could improve response and preparedness efforts. Problemsolving efforts should be the focus.
Assumptions and Artificialities:
In any exercise, assumptions and artificialities may be necessary to complete play in the
time allotted. During this exercise, the following apply:
•
•
•
The scenario is plausible, and events occur as they are presented.
There is no hidden agenda, and there are no trick questions.
All players receive information at the same time.
412
Part II- Scenario
Day 1: Tuesday, November 18, 2010
It's a football game between Gateway Regional High School and Highland High School
at Highland, located in Blackwood, NJ.
This is the Homecoming game and is heavily attended by students, alumni, family, and
members of the community.
A suspicious-looking man, wearing a cap and sunglasses, is observed using a hand-held
aerosol-dispersion device, spraying some material around the bleachers and the snack bar
area at the stadium.
Observers file a report with a school security guard, noting the suspicious behavior. The
suspect, having fled the scene, cannot be apprehended for questioning.
The school security guard initiates investigation and notifies law enforcement officials.
The HAZMAT team and law enforcement officials arrive on the scene and secure the
area.
Handheld assays yield preliminary results within two hours, suggesting that an anthrax
exposure has occurred
Samples of the agent are collected for more sensitive and specific confirmatory testing at
the state lab. Results will take 1-2 days.
Players and fans alike leave the game eager to share news of the commotion
with friends and family.
Law enforcement officials call the duty officer at the Camden County Department of
Health and Human Services (CCDHHS) and notify him of the potential, yet
unconfirmed, anthrax attack at the football game.
Day 1 Questions
• What steps should the health department take while waiting for confirmatory lab
results?
• Would you activate the incident command system within the health department?
• Would you activate the Emergency Operations Center?
• Is there a need to communicate any information to response partners or the
public?
413
Day 2: Wednesday, November 19, 2010
The state lab confirms anthrax as agent in question.
Day 2 Questions 1
• What information should be considered to determine whether SNS assets are
necessary?
• With whom should the Health Officer consult regarding public health response
actions?
• Who else within the CCDHHS should be notified of this incident and potential
response actions?
• Which agencies and jurisdictions outside of the CCDHHS should be notified of
this incident?
Day 2: Wednesday, November 19,2010
At 9am, the CCDHHS holds a conference call with the Camden County OEM, the
Camden County Prosecutors Office, the two high schools, along with the NJDHSS, to
discuss the situation.
During the conference call, the decision is made by the CCDHHS to request supplies
from the Strategic National Stockpile.
A mass dispensing clinic must be activated to deliver antibiotics to potentially exposed
individuals.
Day 2
•
•
•
•
•
•
Questions 2
Who makes the request for SNS assets? To whom is the request made?
Where will the warehouse be? And how will you gain access?
Who is considered essential warehouse personnel?
How are essential warehouse personnel alerted?
What will they need to do before receipt of SNS materials?
What security measures are put into place, and when will they need to be
implemented?
• How will information regarding this operation be communicated to the public and
those potentially exposed individuals?
Day 2: Wednesday, November 19, 2010
State DHSS coordinates a teleconference with the Governor's Office, CDC, and
the local health department.
The Governor formally requests SNS deployment.
Following the conference call, the CDC director orders the deployment of SNS assets to
the state.
414
Day 3: Thursday November 20, 2010
CCDHHS receives SNS assets at their warehouse.
The CCDHHS will begin Point of Delivery (POD) operations at a local venue.
Day 3
•
•
•
•
Questions 1 Receipt and Activation
How are staff alerted for Primary RSS Operations?
What, if any, just-in-time training is required?
Who is the Incident Commander?
Who will be receiving SNS assets?
Day 3 Questions 2 Activation and Use/Distribution
• How will the assets be unloaded, separated, and distributed to POD sites?
How will the PODS receive the materials?
• How will the PODS manage inventory?
• How will the PODS maintain security during distribution?
415
Day 5: Saturday, November 22, 2010
The need being met, the decision is made by the CCDHHS to discontinue POD
operations and demobilize and then close RSS operations.
SNS supplies are no longer being received.
Day 5
•
•
•
•
•
Questions - Shut Down
Who needs to be notified?
What personnel issues need to be considered?
What inventory needs to be done?
What needs to be done to the facility?
Who is responsible for the Hot Wash?
416
Part III-Appendix
417
Prior to Receipt (Pre-RSS Operations)
White background:
Done completely
outside of the
Warehouse
I Decision has been made to activate SNS'
plan
Request to NJDHSS via
LINCS jurisdiction's OEM to
State OEM for SNS Materials
Request for use of
RSS & Gaining
Access to RSS
Request for
security
deployment to
RSS
Olive Green background: Done
partially inside the Warehouse and
partially outside the Warehouse.
Additionally, some things may start
at one and transition to the other or
be done equally well in either
setting, depending on the local
situation.
Blue background:
iDone completely insidethe Warehouse
Recall of RSS
Staff (Notification
& Reporting)
Just-In-Time
Training of
RSS Stt*
AoqulsHonr
Staging of
Equipment and
Supplies
Notification to
Command of RSS
Readiness
(RSS Stated* Ready to A
Receive SNS Assets
J
418
Primary Operations for RSS
Receipt of assets
from Slate RSS
Unloading of
assets
Separation of
assets by type
(Staging)
i r
Inventory or assets
received
Need more assets-
Request additional
assets as required
Notify receiving
agencies of partial
shipments
•HSU QJ LOtWlUK)
e of assets to outsidA
locations
/
419
Repackaging of
assets (It naaaad)
Post RSS Operations
/
\
I Order from Command to 1
I
close RSS
J
Terminate exterior
deliveries of
assets
Inventory assets to
be returned
Notification to
NJDHSS of assets
to be returned
Repackage assets
tor return
Return of Excess
*> NJDHSS
Return of Facility
to original
MHOJLi
atait
Hotwash of RSS
Operation
Release of
Personnel
f Shut down of RSSJ
420
yA^nthrax
What Is anthrax?
*
Anthrax is a serious disease caused by Bacillus anthracis, a bacterium (germ) that forms spores. A
spore is a cell that is dormant (asleep) but may come to life with the right conditions. There are three
types of anthrax:
• Cutaneous (skin)
•Gastrointestinal (digestive)
-inhalation (lungs)
What are the symptoms?
Cuteneoi/s-The first symptom is a small sore that develops into a blister. The blister then develops
into a skin ulcer with a black area in the center. The sore, blister and ulcer do not hurt.
Gasfro/ntesft'na/-The first symptoms are nausea, loss of appetite, bloody diarrhea, and fever, followed
by bad stomach pain.
Inhalation-Tbe first symptoms of inhalation anthrax are like cold or flu symptoms and can include a
sore throat, mild fever and muscle aches. Later symptoms include cough, chest discomfort, shortness
of breath, tiredness and muscle aches.
How soon do Infected people get sick?
Symptoms can appear within 7 days of coming in contact with the spores for all three types of
anthrax. For inhalation anthrax, symptoms can appear within a week or can take up to 60 days to
appear.
Is anthrax contagious?
Anthrax is not known to spread from one person to another. People can become infected with
anthrax by handling products from infected animals or by breathing in anthrax spores from infected
animal products like raw, untreated wool. People also can become infected with gastrointestinal
anthrax by eating undercooked meat from infected animals.
How is anthrax treated?
Antibiotics treat all three types of anthrax. Early identification and treatment are important. Success
depends on the type of anthrax and how soon treatment begins.
Can anthrax be prevented?
There is a vaccine to prevent anthrax, but it is not currently available for the general public. In the
event of an anthrax attack, healthcare providers will administer vaccine and antibiotics to people who
may have been exposed to 0. anthracis, but are not sick.
What should I do If I think I have anthrax?
If you are showing symptoms of anthrax infection, call your healthcare provider right away.
421
Can anthrax be used as a biological weapon?
Anthrax has already been used as a weapon. This happened in the United States In 2001 In New
Jersey and elsewhere. Anthrax was deliberately spread through the postal system by sending
letters with powder containing anthrax. This caused 22 cases of anthrax infection. Five cases
occurred in New Jersey, with no deaths.
How dangerous Is anthrax?
The Centers for Disease Control and Prevention (CDC) classifies agents with the potential to be
used for bioterrorism into three categories: A, B and C. Anthrax is a Category A agent.
Category A agents:
• pose the greatest possible threat to the public's health
• may spread across a large area
• require advance planning to protect the public's health
In most cases, early treatment with antibiotics can cure cutaneous anthrax. Even if untreated,
80 percent of people who become infected with cutaneous anthrax do not die. Gastrointestinal
anthrax is more serious. Between 25 and 50 percent of cases result In death. Inhalation
anthrax is much more severe. In 2001, about half of the cases of inhalation anthrax in the
United States died.
What Is New Jersey doing to prepare for a possible anthrax attack?
New Jersey Is working with the CDC to prepare for an anthrax attack. Activities include:
• Developing plans and procedures to respond to an anthrax attack
•Training and equipping emergency response teams, gathering samples and
performing tests to help state and local governments control Infection
• Educating healthcare providers, the media, and the general public about
what to do in the event of an attack
• Working closely with local health departments, veterinarians and
laboratorlans to watch for suspected cases of anthrax
•Working with hospitals, laboratories, emergency response teams, and
healthcare providers to make sure they have the supplies they need in case
of an attack
Where can I get more information?
• Your healthcare provider
• Your local department of health
•The New Jersey Department of Health and Senior Services
--Website - www.nj.gov/health
—DHSS Communicable Disease Service at (609) 826-5964
•CDC
--www.bt.cdc.gov/agent/anthrax
"1-800-CDC-INFO (4636) for assistance in English and Spanish
--TTY 1-888-232-6348
-E-mail: cdcinfo@cdc.gov
Revised 2^011
422
8/3/2011
10 RSS Scenario Presentation
Receipt, Stage, and Storage (RSS)
Warehouse Operations:
A Tabletop Exercise
Camden County
April 14, 2011
QuickTime*" and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Introductions
Facilitators
Players
Observers
423
8/3/2011
Housekeeping
• Keep all cell phones, blackberries, pagers,
etc., on vibrate.
• Take breaks as you need them.
• Restrooms are located. . .
Purpose
The purpose of the Camden County
Receipt, Stage, and Storage (RSS)
Warehouse Operations: A Tabletop
Exercise is to coordinate RSS operations
in Camden County. The exercise will test
the capabilities of current plans, policies,
and procedures related to RSS.
424
8/3/2011
Scope
The scope of this exercise will focus on
the various emergency responders' roles
in response to a bioterror attack and the
stages of RSS operations. More important
than the minute details are the processes
and decision-making. The emphasis
should be on coordination, integration,
problem identification, and problem
resolution.
Learning Objectives
1
To identify shortfalls in resources, limits in
capabilities, and gaps in planning and
coordination for RSS.
2
To exercise the local decision-making process
and identify areas needing refinements.
Review and list the various roles, functions, and
procedures involved in RSS activation.
Identify issues relevant to RSS activation and
preparing for mass dispensing, e.g., policies,
resources, communication, coordination, and data
management
3
4.
425
8/3/2011
Exercise Agenda
9:30- Welcome and Overview
10:00-Exercise
11:40- Hot Wash
12:00-Adjourn
What is a tabletop exercise?
Informal group discussion stimulated by a
scripted disaster scenario
Cross-agency exercise designed to
promote free and open exchange of ideas
Emphasis on training and learning, not
testing—helps prepare participants for a
full-scale or functional exercise
Evaluation of current systems, response
plans
426
8/3/2011
Instructions to Remember
Assume scenario is real
Be AS SPECIFIC AS POSSIBLE
• Names, titles, streets, places, etc.
Make your best decision based on available
information
Play your department or agency role
throughout
Instructions to Remember
Consider policy issues as well as procedure
Focus on identifying system gaps and
strengths rather than deficits in individual
knowledge
Take notes for the debriefing discussion(e.g.,
gaps and strengths in resource planning,
communication, information management)
427
8/3/2011
Day 1: Tuesday, 11-18-2010
It's a football game between Gateway
Regional High School and Highland High
School at Highland, located in Blackwood, NJ.
This is the Homecoming game and is heavily
attended by students, alumni, family, and
members of the community.
428
6
8/3/2011
Day 1: Tuesday, 11-18-2010
•A suspicious-looking man, wearing a cap and
sunglasses, is observed using a hand-held
aerosol-dispersion device, spraying some
material around the bleachers and the snack
bar area at the stadium.
•Observers file a report with a school security
guard, noting the suspicious behavior. The
suspect, having fled the scene, cannot be
apprehended for questioning.
Day 1: Tuesday, 11-18-2010
The school security guard initiates
investigation and notifies law enforcement
officials.
The HAZMAT team and law enforcement
officials arrive on the scene and secure
the area.
Handheld assays yield preliminary results
within two hours, suggesting that an
anthrax exposure has occurred
429
8/3/2011
Day 1: Tuesday, 11-18-2010
Samples of the agent are collected for
more sensitive and specific confirmatory
testing at the state lab.
Results will take 1-2 days.
Players and fans alike leave the game
eager to share news of the commotion
with friends and family.
Day 1: Tuesday, 11-18-2010
Law enforcement officials call the duty
officer at the Camden County Department
of Health and Human Services (CCDHHS)
and notify him of the potential, yet
unconfirmed, anthrax attack at the football
game.
430
8
8/3/2011
Day 1 Questions
• What steps should the health department
take while waiting for confirmatory lab results?
• Would you activate the incident command
system within the health department?
• Would you activate the Emergency
Operations Center?
• Is there a need to communicate any
information to response partners or
the public?
Day 2: Wednesday, 11 -19-10
The state lab confirms anthrax as
agent in question.
431
8/3/2011
Day 2 Questions 1
•What information should be considered to
determine whether SNS assets are
necessary?
•With whom should the Health Officer consult
regarding public health response actions?
•Who else within the CCDHHS should be
notified of this incident and potential
response actions?
-Which agencies and jurisdictions outside of
the CCDHHS should be notified of this
incident?
Day 2[Wednesday 11-19-2010
At 9am, the CCDHHS holds a conference
call with the Camden County OEM, the
Camden County Prosecutors Office, the two
high schools, along with the NJDHSS, to
discuss the situation
During the conference call, the decision is
made by the CCDHHS to request supplies
from the Strategic National Stockpile
A mass dispensing clinic must be activated
to deliver antibiotics to potentially exposed
individuals.
432
10
8/3/2011
Day 2 Questions 2
Who makes the request for SNS assets?
To whom is the request made?
Where will the warehouse be? And how will
you gain access?
Who is considered essential warehouse
personnel?
How are essential warehouse personnel
alerted?
Day 2 Questions 3
What will they need to do before receipt of
SNS materials?
What security measures are put into place,
and when will they need to be implemented?
How will information regarding this operation
be communicated to the public and those
potentially exposed individuals?
433
11
8/3/2011
Day 2:Wednesday 11-19-2010
State DHSS coordinates a teleconference
with the Governor's Office, CDC, and
the local health department.
The Governor formally requests SNS
deployment.
Following the conference call, the CDC
director orders the deployment of SNS
assets to the state.
Day 3:Thursday 11-20-2010
CCDHHS receives SNS assets at their
warehouse.
The CCDHHS will begin Point of
Delivery (POD) operations at a local
venue.
434
12
8/3/2011
Day 3 Questions 1 Receipt and
Activation
How are staff alerted for Primary RSS
Operations?
What, if any, just-in-time training is required?
Who is the Incident Commander?
Who will be receiving SNS assets?
Day 3 Questions 2 Activation
and Use/Distribution
How will the assets be unloaded, separated,
and distributed to POD sites?
How will the PODS receive the materials?
How will the PODS manage inventory?
How will the PODS maintain security during
distribution?
435
13
8/3/2011
Day 5: Saturday, 11-22-2010
The need being met, the decision is
made by the CCDHHS to discontinue
POD operations and demobilize and
then close RSS operations.
SNS supplies are no longer being
received.
Day 5 Questions - Shut Down
Who needs to be notified?
What personnel issues need to be
considered?
What inventory needs to be done?
What needs to be done to the facility?
Who is responsible for the Hot Wash?
436
14
8/3/2011
(Our Own) Hot Wash
Your Ideas Here!
437
15
11 RSS Participant Feedback Form
Participant Feedback Form
Name:
(optional)
Agency:
(optional)
Title:
(optional)
Part I: Recommendations and Corrective Actions
List the top three demonstrated strengths after today's exercise.
2. List the top three areas that need improvement after today's exercise.
438
3. Identify the action steps that should be taken to address the issues you identified in questions
1. and 2.
4. List the policies, plans, and procedures that should be reviewed, revised, or developed for your
agency.
Part II: Participant Feedback
What changes would you make to this exercise overall? Please provide any recommendations on
how this exercise or future exercises could be improved or enhanced.
439
12 MA Facilitator's Situation Manual
Capital District Urban Areas Security
Initiative (UASI) Mutual Aid Tabletop
Exercise
March 24, 2011
Developed by
The Urban Areas Security Initiative (UASI) Public Health Subcommittee and the NY»NJ Preparedness and Emergency
Response Learning Center (PERLC)
440
Part I- Introduction
Exercise Name
Capital District UASI Mutual Aid Tabletop Exercise
Exercise Purpose and Design
The purpose of the Capital District UASI TTX exercise is to inform and test mutual aid protocols
between the policy makers, planners and coordinators of four Capital District counties (Albany,
Rensselaer, Schenectady, Schoharie) in the event of a public health emergency. The intent of
the exercise is to develop processes and protocols to initiate, maintain, track and recover critical
personnel resources during a public health emergency at the agency level. A combination of
discussion oriented pre-event planning workshops and a tabletop exercise was developed and
delivered for the goals of this exercise.
This exercise is funded by the Homeland Security (HS) Urban Areas Security Initiative (UASI)
grant intended to address and assist multi-discipline preparedness and emergency operations for
high-threat urban areas. The exercise planning was a collaborative initiative between the
Albany, Rensselaer, Schenectady, and Schoharie County public health departments. The NY-NJ
PERLC was requested as a coordinating and advising agency to help with exercise development,
facilitation, and evaluation.
Exercise Objectives, Capabilities and Activities
Capabilities-based planning allows for exercise planning teams to develop exercise objectives
and observe exercise outcomes through a framework of specific action items that were derived
from the Target Capabilities List (TCL). The capabilities listed below form the foundation for
the organization of all objectives and observations in this exercise. Additionally, each capability
is linked to several corresponding activities and tasks to provide additional detail. The capability
selected for this exercise is Critical Resource Logistics and Distribution under the Response
Mission.
The following are the Capital District UASI Mutual Aid Tabletop Exercise objectives and the
capabilities to be demonstrated during this exercise:
Objective 1:
Identify and define each county's mutual aid agreements as utilized for the request, release and
recall of skilled personnel during public health emergencies.
o Capability: Critical Resource Logistics and Distribution:
Activ ty 1: Direct Critical Resource Logistics(CRL) and Distribution
Activ ty 2: Activate Critical Resource Logistics and Distribution
Activ ty 3: Respond to Needs Assessment and Inventory
Activ ty 4: Acquire Resources
Activ ty 5: Transport, Track and Manage Resources
Activ ty 6: Maintain and Recover Resources
Activ ty 7: Demobilize Critical Resource Logistics and Distribution
441
Objective 2:
Determine how mutual aid and continuity of operations planning and operations interact when
allocating skilled personnel during a public health emergency.
o Capability: Critical Resource Logistics and Distribution:
• Activity 5: Transport, Track and Manage Resources
• Activity 6: Maintain and Recover Resources
Objective 3:
Identify and define considerations informing the planning and allocation of skilled personnel
during a regional public health emergency response effort in the Capital District counties.
Factors included in this planning include:
a. Duration of the emergency.
b. Forms needed for resource sharing.
c. General guidance sheet for determining request, release and recall of skilled
personnel.
o Capability: Critical Resource Logistics and Distribution:
i. Activity 1: Direct Critical Resource Logistics(CRL) and Distribution
ii. Activity 3: Respond to Needs Assessment and Inventory
iii. Activity 5: Transport, Track and Manage Resources
Objective 4:
Practice protocols for requesting, releasing, tracking and recovering skilled personnel during a
public health emergency driven scenario during the tabletop exercise.
o Capability: Critical Resource Logistics and Distribution:
• Activity 1: Direct Critical Resource Logistics(CRL) and Distribution
• Activity 2: Activate Critical Resource Logistics and Distribution
• Activity 3: Respond to Needs Assessment and Inventory
• Activity 4: Acquire Resources
• Activity 5: Transport, Track and Manage Resources
• Activity 6: Maintain and Recover Resources
• Activity 7: Demobilize Critical Resource Logistics and Distribution
442
Participants
•
•
•
•
Players: Emergency Manager, BT Coordinator/DOH representatives from each of
the participating counties
Facilitators
Evaluators
Observers
Exercise Agenda
10:00-10:10
10:10-10:15
10:15-11:00
11:00-11:15
11:15-12:00
12:00-12:45
12:45-1:15
1:15-2:00
2:00
Welcome
Introduction to TTX
Scenario 1
Break
Scenario II
Lunch
Scenario III
Hot Wash and Evaluation
Adjourn
Exercise Guidelines
This TTX will be held in an open, low-stress, no-fault environment. Varying viewpoints, even
disagreements, are expected. Respond on the basis of your knowledge of current plans and
capabilities (i.e., you may use only existing assets) and insights derived from your training.
Decisions are not precedent setting and may not reflect your organization's final position on a
given issue. This exercise is an opportunity to discuss and present multiple options and possible
solutions. Issue identification is not as valuable as suggestions and recommended actions that
could improve response and preparedness efforts. Problem-solving efforts should be the focus.
Assumptions and Artificialities
In any exercise, assumptions and artificialities may be necessary to complete play in the time
allotted. During this exercise, the following apply:
•
•
•
The scenario is plausible, and events occur as they are presented.
There is no hidden agenda, and there are no trick questions.
All players receive information at the same time.
443
Notes for Facilitators and Evaluators
Lead Facilitator RoleThe lead facilitator will guide the participants through the exercise. He will explain the purpose
and objectives of the exercise, define expectations of the participants, and provide instructions
and keep the exercise moving in accordance with the time allotted. The lead facilitator will
present the scenario and the questions using the PowerPoint presentation. He will also act as a
county facilitator. He will lead the group report out and discussion, soliciting responses from the
group at each "Questions" slide.
County Facilitator RoleFor all three sections of the scenario, there will be a facilitator seated with each county. There
will be fifteen minutes of discussion during that time within each county to react and respond to
the scenario. During that time, the facilitator will solicit response from their county using the
provided questions after each scenario as a guide. The lead facilitator will then lead a twentyminute discussion with all of the counties. Discussion between the counties is permitted, and
encouraged when it is appropriate to further the scenario.
Evaluators' RoleAn evaluator will be seated with each county. Each evaluator will be given an Exercise
Evaluation Guide (EEG) and will use the EEG as a guide for note taking. Evaluators will:
•
•
•
Identify strengths, weaknesses, and unanticipated responses to the scenario.
Identify communication problems and needed additional resources
Identify consistency with the exercise objectives, activities in the target capability, and
related accomplishments.
444
Part 2- Scenario
Day 1- Monday
It is the beginning of spring in the Capital Region of New York state and the snow is finally
beginning to melt and the flu season is drawing to a close. However, there has been a mixture of
Influenza A and gastrointestinal illnesses being reported.
Within an eight hour period, twelve (12) patients, including a family of four (4) have been seen
at the Cobleskill Regional Hospital emergency department; on a typical Monday, 1 or 2 might be
seen in an entire 24 hour period. Symptoms included diarrhea, fever, nausea, headache, and
cramping. The four family members have been admitted to the local hospital, with two of the
four transferred to pediatrics. The hospital has notified the Schoharie County Health
Department.
Questions
•
•
•
•
•
•
•
Is further investigation warranted? Explain.
What type of surveillance activities will you undertake at this time?
Given what you learn, what actions would you take, if any?
Who would you notify at this point?
Are any additional resources needed at this time?
Is a request for aid from other counties made at this point?
What mutual aid agreements (MAAs) do you currently have in place that could be used for
this response?
• What aid is the county requesting?
• If so, how is that letter of request drafted and sent?
• What considerations are made for issues of liability, finance, workman compensation?
445
Day 2- Tuesday
The Schoharie County HD has contacted the Schenectady County HD to assist in the disease
investigation, since the number of patients treated at the hospital ED for gastrointestinal
symptoms has grown to a total of eighteen (18). A case history has been obtained on each patient
and the hospital physician has ordered stool specimens for analysis for those patients in the
hospital, and any new patients that present to the ED. Schoharie County public health authorities
have issued a Health Alert to inform providers of the situation and stimulate reporting.
Surveillance activities have been expanded, with calls going to local pediatric and general
practitioners
There has also been an increase in the number of gastrointestinal illnesses being reported through
surveillance monitoring. Hospitals in Schenectady County are now reporting an abnormally high
number of similar cases.
Questions
•
•
•
•
•
•
What actions would you take?
What local, state, or federal agencies would you notify?
Should the Emergency Operations Center (EOC) be activated? If not, when would it be?
Are any additional resources needed at this time?
Would mutual aid be requested now?
o What aid is the county requesting?
o What types of personnel are being requested?
o If so, how is that letter of request drafted and sent?
o What considerations are made for issues of liability, finance, and workman
compensation?
How are enacted Mutual Aid Agreements handled?
446
Day 3- Wednesday
There have now been cases in hospitals in Albany and Rensselaer counties, with thirty-one (31)
reported within the last 24 hours. Still more patients are being admitted to all Capital Region area
hospitals, while other patients are being seen in the emergency rooms, urgent care and public
health clinics. Private physicians have called in to tell of crowded waiting rooms with ill patients
and multiple phone calls from the 'worried well'.
Patient interviews reveal a possible link to five (5) area fast food establishments of the same
chain. The food histories reveal the cases appear to have eaten at the fast food establishments
within 24-36 hours of the development of their symptoms. Environmental Health staff members
have been dispatched to the affected food establishments for an investigation.
Questions
• What actions would you take?
• Who would you notify at this point?
• What, if any, additional resources are needed at this time?
• Would a request for aid from other counties be made at this point?
o What aid is the county requesting?
o What types of personnel are being requested?
o If so, how is that letter of request drafted and sent?
o What considerations are made for issues of liability, finance, and workman
compensation?
• Will counties continue to support other counties now that the outbreak is in their county?
• What is the procedure to end an agreement?
Epilogue
A week has passed since the first cases in Schoharie County and laboratory results have
confirmed that 16 patient samples grew out salmonella with the remaining cases suspected, but
unconfirmed.
The source of the salmonella is believed to have come from milk that was sold at fast food
establishments in the Capital Region. Environmental health staff have embargoed the suspect
milk and contacted the distributor.
447
13 MA Players' Situation Manual
Capital District Urban Areas Security
Initiative (UASI) Mutual Aid Tabletop
Exercise
March 24, 2011
Developed by
The Urban Areas Security Initiative (UASI) Public Health Subcommittee and the NY»NJ Preparedness and Emergency
Response Learning Center (PERLC)
448
Part I- Introduction
Exercise Name
Capital District UASI Mutual Aid Tabletop Exercise
Exercise Purpose and Design
The purpose of the Capital District UASI TTX exercise is to inform and test mutual aid protocols
between the policy makers, planners and coordinators of four Capital District counties (Albany,
Rensselaer, Schenectady, Schoharie) in the event of a public health emergency. The intent of
the exercise is to develop processes and protocols to initiate, maintain, track and recover critical
personnel resources during a public health emergency at the agency level. A combination of
discussion oriented pre-event planning workshops and a tabletop exercise was developed and
delivered for the goals of this exercise.
This exercise is funded by the Homeland Security (HS) Urban Areas Security Initiative (UASI)
grant intended to address and assist multi-discipline preparedness and emergency operations for
high-threat urban areas. The exercise planning was a collaborative initiative between the
Albany, Rensselaer, Schenectady, and Schoharie County public health departments. The NY-NJ
PERLC was requested as a coordinating and advising agency to help with exercise development,
facilitation, and evaluation.
Exercise Objectives, Capabilities and Activities
Capabilities-based planning allows for exercise planning teams to develop exercise objectives
and observe exercise outcomes through a framework of specific action items that were derived
from the Target Capabilities List (TCL). The capabilities listed below form the foundation for
the organization of all objectives and observations in this exercise. Additionally, each capability
is linked to several corresponding activities and tasks to provide additional detail. The capability
selected for this exercise is Critical Resource Logistics and Distribution under the Response
Mission.
The following are the Capital District UASI Mutual Aid Tabletop Exercise objectives and the
capabilities to be demonstrated during this exercise:
Objective 1:
Identify and define each county's mutual aid agreements as utilized for the request, release and
recall of skilled personnel during public health emergencies.
o Capability: Critical Resource Logistics and Distribution:
• Activity 1: Direct Critical Resource Logistics(CRL) and Distribution
• Activity 2: Activate Critical Resource Logistics and Distribution
• Activity 3: Respond to Needs Assessment and Inventory
• Activity 4: Acquire Resources
• Activity 5: Transport, Track and Manage Resources
• Activity 6: Maintain and Recover Resources
• Activity 7: Demobilize Critical Resource Logistics and Distribution
449
Objective 2:
Determine how mutual aid and continuity of operations planning and operations interact when
allocating skilled personnel during a public health emergency.
o Capability: Critical Resource Logistics and Distribution:
• Activity 5: Transport, Track and Manage Resources
• Activity 6: Maintain and Recover Resources
Objective 3:
Identify and define considerations informing the planning and allocation of skilled personnel
during a regional public health emergency response effort in the Capital District counties.
Factors included in this planning include:
a. Duration of the emergency.
b. Forms needed for resource sharing.
c. General guidance sheet for determining request, release and recall of skilled
personnel.
o Capability: Critical Resource Logistics and Distribution:
i. Activity 1: Direct Critical Resource Logistics(CRL) and Distribution
ii. Activity 3: Respond to Needs Assessment and Inventory
iii. Activity 5: Transport, Track and Manage Resources
Objective 4:
Practice protocols for requesting, releasing, tracking and recovering skilled personnel during a
public health emergency driven scenario during the tabletop exercise.
o Capability: Critical Resource Logistics and Distribution:
• Activity 1: Direct Critical Resource Logistics(CRL) and Distribution
• Activity 2: Activate Critical Resource Logistics and Distribution
• Activity 3: Respond to Needs Assessment and Inventory
• Activity 4: Acquire Resources
• Activity 5: Transport, Track and Manage Resources
• Activity 6: Maintain and Recover Resources
• Activity 7: Demobilize Critical Resource Logistics and Distribution
450
Participants
•
•
•
•
Players: Emergency Manager, BT Coordinator/DOH representatives from each of
the participating counties
Facilitators
Evaluators
Observers
Exercise Agenda
10:00-10:10
10:10-10:15
10:15-11:00
11:00-11:15
11:15-12:00
12:00-12:45
12:45-1:15
1:15- 2:00
2:00
Welcome
Introduction to TTX
Scenario 1
Break
Scenario II
Lunch
Scenario III
Hot Wash and Evaluation
Adjourn
Exercise Guidelines
This TTX will be held in an open, low-stress, no-fault environment. Varying viewpoints, even
disagreements, are expected. Respond on the basis of your knowledge of current plans and
capabilities (i.e., you may use only existing assets) and insights derived from your training.
Decisions are not precedent setting and may not reflect your organization's final position on a
given issue. This exercise is an opportunity to discuss and present multiple options and possible
solutions. Issue identification is not as valuable as suggestions and recommended actions that
could improve response and preparedness efforts. Problem-solving efforts should be the focus.
Assumptions and Artificialities
In any exercise, assumptions and artificialities may be necessary to complete play in the time
allotted. During this exercise, the following apply:
•
•
•
The scenario is plausible, and events occur as they are presented.
There is no hidden agenda, and there are no trick questions.
All players receive information at the same time.
451
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15 MA Participant Feedback Form
Participant Feedback Form
Name:
(optional)
Agency:
(optional)
Role:
Player LJ
Title:
(optional)
Facilitator Q
Observer Q
Part I: Recommendations and Corrective Actions
1. List the top three demonstrated strengths after today's exercise.
2. List the top three areas that need improvement after today's exercise
482
Evaluator
3. Identify the action steps that should be taken to address the issues you identified in questions
1.and 2.
4. List the policies, plans, and procedures that should be reviewed, revised, or developed for your
agency.
483
Part II: Assessment of Training Session and Exercise Design and Conduct
Please rate, on a scale of 1 to 5, your overall assessment of the exercise relative to the statements
provided below, with 1 indicating strong disagreement with the statement, 5 indicating strong
agreement, and 7 indicating don't know or not applicable.
Assessment Factor
a. The exercise was well
structured and organized.
b. The exercise scenario was
plausible and realistic.
c. The facilitators were
knowledgeable about the material,
kept the exercise on target, and
were sensitive to group dynamics.
d. The materials provided were
valuable tools throughout the
exercise.
e. Participation in the exercise was
appropriate for someone in my
position.
f. The participants included the
right people in terms of level and
mix of disciplines.
g. The arrangements (facility,
location, food) were suitable.
N
%
N
%
Strongly
Disagree
1
2
0
4
0.0
16.0
0
1
4.2
0.0
3
4
16.0
5
20.8
Strongly
Agree
4
5
5
12
20.0 48.0
10
8
41.7 33.3
Total
25
100.0
24
100.0
Mean
4.0
4.0
0
0.0
1
4.3
4
17.5
9
39.1
9
39.1
23
100.0
4.1
%
1
4.2
1
4.2
9
37.5
9
37.5
4
16.6
24
100.0
3.6
N
%
0
0.0
0
0.0
2
8.3
4
16.7
18
75.0
24
100.0
4.7
N
0
0.0
0
0.0
0
0.0
0
0.0
1
4.2
2
8.3
9
37.5
2
8.3
14
58.3
20
83.4
24
100.0
24
100.0
N
%
N
%
N
%
L
4.5
4.8
Part III: Participant Feedback
What changes would you make to this exercise overall? Please provide any recommendations on
how this exercise or future exercises could be improved or enhanced.
484