. Author manuscript; available in PMC: 2013 Jul 2.

Published in final edited form as: Med Decis Making. 2009 Jul 15;29(4):438–460. doi: 10.1177/0272989X09340346

Table 1.

Description of Selected Health Sector Disaster Response Models

Author (Reference)	Purpose of Model/Study	Modeling Methodology	Disaster Evaluated^†	Decision Makers Considered^*	Geographic Setting	Decisions Modeled^§	Outcomes^¶
Disease Outbreaks
Braithwaite, Fridsma, Roberts¹	To assess the cost-effectiveness of pre-exposure anthrax vaccination vs. an emergency surveillance and response system	Dynamic compartmental model	Anthrax	P	Local and Regional	Rx, P	$, QALY, M
Hupert, Mushlin, Callahan^{3, 9}	To determine staffing levels necessary to maintain throughput requirements for antibiotic dispensing centers in the aftermath of a bioterrorism attack	Simulation (Discrete event)	Anthrax	P, PH	Local	D, S	L
Lee and others^{34, 35}	To construct and implement a real-time decision support system for planning antibiotic dispensing in response to a large-scale disease outbreak	Simulation, Optimization	Anthrax	P, PH	Local (Urban setting)	D, S	L
Brookmeyer, Johnson, Bollinger^{11, 79}	To assess the optimum duration of antibiotic prophylaxis and evaluate varying prophylactic strategies (pre or post-exposure vaccination, antibiotic prophylaxis) for anthrax response	Competing risks, probability/mathematical model	Anthrax	P, PH	Local	Rx, P	M
Wein, Craft, and others^66–68	To evaluate the effectiveness of several response strategies for anthrax (pre-exposure vaccination, achievable levels of distributed antibiotic prophylaxis, biosensor efficacy)	Multi-tiered mathematical model	Anthrax	P	Local (A large city)	I, ND, Rx, P	L, M
Fowler and others⁶²	To assess the cost-effectiveness of vaccination (pre- or post-exposure) vs. post-exposure antibiotic prophylaxis response strategies for anthrax	Simulation (Decision analytic)	Anthrax	P	Local (A large US metropolitan area)	Rx, P	$, QALY, M
Schmitt and others⁶³	To evaluate the cost-effectiveness of vaccination (pre- or post-exposure) vs. post-exposure antibiotic prophylaxis response strategies for anthrax response	Simulation (Markov model)	Anthrax	P	Regional (Attack via US Postal Service)	Rx, P	$, QALY, M
Zaric, Bravata, Brandeau, and others^{5, 41, 52}	To evaluate the cost-effectiveness of alternative strategies for maintaining and dispensing antibiotic inventories (local vs. regional) and communication with the public during an anthrax response	Dynamic compartmental model	Anthrax	P	Local (A large US metropolitan area)	I, ND, P	$, QALY, M
Whitworth⁴⁰	To evaluate plans for anthrax response (e.g., number and location of dispensing centers, dispensing strategies, staffing plans, and traffic-management plans)	Simulation (Discrete event)	Anthrax	P	Local	D, S	L
Medema and others⁶⁰	To evaluate health and economic outcomes of interventions for pandemic influenza (e.g., increasing the vaccine supply through egg-based or cell culture, provision of antivirals)	Simulation	Pandemic influenza	P, O	National	Rx	$, L, M
van Genugten, Heijnen, Jager⁵⁴	To estimate (using FluSurge⁴) hospitalizations and deaths in the Netherlands from pandemic influenza, as a function of response strategy (no intervention, vaccinate high-risk individuals, vaccinate all, treat symptomatic people with antiviral drugs)	Spreadsheet	Pandemic influenza	PH	National (Netherlands)	P, Rx	M, H
Longini and others⁵⁶	To investigate the effectiveness of targeted use of antivirals to contain the first wave of an influenza pandemic in the United States (before a vaccine can be developed)	Simulation (Stochastic, discrete time, network of 2000 individuals)	Pandemic influenza	PH	“A typical American community”	P, Rx	O
Meltzer, Cox, Fukuda⁵⁷	To estimate outcomes of pandemic influenza (illnesses, deaths, etc.) and the effects of potential vaccination strategies, and to determine how much should be spent each year to plan/prepare for mass vaccination	Simulation (Monte Carlo)	Pandemic influenza	P	National (US)	P	$, M, H
Eichner and others⁵³	To evaluate the impact of three types of interventions on pandemic influenza outcomes: antivirals, social distancing, and contact reduction	Compartmental epidemic model (Deterministic)	Pandemic influenza	P	National, Regional, or Local	P	M, L, H
Wilson, Mansoor, Baker²⁶	To estimate population health and economic impacts of the next influenza pandemic in New Zealand	Deterministic model	Pandemic influenza	P	National (New Zealand)	P	$, H
Zhang, Meltzer, Wortley⁴	To estimate the impact of pandemic influenza on hospital services	Spreadsheet	Pandemic influenza	H, P	Regional	Rx	H, M
Soberiaj and others²⁵	To estimate the impact of pandemic influenza on hospital services at the William Beaumont Army Medical Center in El Paso, Texas	Spreadsheet	Pandemic influenza	H, P	Local	Rx	M
Siddiqui and Edmonds⁵⁹	To evaluate the cost-effectiveness of antiviral stockpiling and near-patient testing (rapid diagnostic tests at point of care) for an influenza pandemic in the United Kingdom	Spreadsheet (Incorporates a decision tree; allows for probabilistic and other sensitivity analyses)	Pandemic influenza	P, PH	National (UK)	I, Rx	$, QALY
Balicer and others⁵⁸	To evaluate the cost-benefit of three different strategies for the use of stockpiled antiviral drugs during an influenza pandemic: therapeutic; long-term pre-exposure prophylaxis (PrEP), and short-term PrEP	Spreadsheet	Pandemic influenza	P, PH	National	I, Rx	$
Germann and others³⁶	To simulate pandemic influenza in the United States and evaluate the effect of potential mitigation strategies, including antivirals, vaccines, and modified social mobility (travel restrictions, school closures)	Simulation (Microsimulation of 281 million individuals in 2000-person subgroups)	Pandemic influenza	P	National (US)	P	M
Khazeni and others⁶¹	To estimate the cost-effectiveness of two control strategies for pandemic influenza: antiviral prophylaxis and prime-boost vaccination	Compartmental epidemic model (Deterministic)	Pandemic influenza	P, PH	Local (A large US metropolitan city)	P	$, QALY
Colizza and others⁸⁰	To evaluate the effect of international travel restrictions and antiviral treatment on the worldwide spread of pandemic influenza	Compartmental epidemic model (Stochastic; linked models, one for each of 3100 cities/airports)	Pandemic influenza	P, PH	Global	P	M
Gupta, Moyer, and Stern⁶⁴	To evaluate the cost-benefit of quarantine in controlling SARS	Mathematical (Deterministic)	SARS	PH	Local (Toronto)	P	$, M
Lloyd-Smith, Galvani, and Getz⁸¹	To evaluate the effects on SARS transmission within a hospital and a community of hospital-based contact precautions, quarantine, and isolation	Compartmental epidemic model (Stochastic)	SARS	H, PH	Local (Hospital and community)	P	M, O
Lipsitch and others⁸²	To evaluate the effects on SARS transmission of quarantine and isolation measures	Compartmental epidemic model (Deterministic)	SARS	PH	Local	P	M, L, O
Massin and others⁵⁵	To evaluate interventions for controlling a pneumonic plague outbreak: masks, quarantine, prophylaxis, travel restrictions	Compartmental epidemic model (Deterministic)	Plague	P	National, Regional	P	M
Kaplan, Craft, Wein⁸³	To compare mass vaccination vs. ring vaccination for responding to a smallpox attack in a major US city	Compartmental epidemic model (deterministic)	Smallpox	PH	Regional	P	M
Meltzer and others⁷⁰	To evaluate the amount of quarantine and vaccination (alone or in combination) that would be required to control a smallpox outbreak caused by bioterrorists, and to estimate the number of vaccine doses needed	Markov model (Spreadsheet)	Smallpox	P	Local	P	M
Miller, Randolph, Patterson⁸⁴	To evaluate the effects on health and the healthcare system of strategies for responding to a smallpox attack, including vaccination (mass vaccination or ring vaccination), social distancing measures, and quarantine	Simulation (Discrete event, modeling individual people)	Smallpox	P	Local	P	M, H
Glasser and others⁷¹	To evaluate the effects of a variety of smallpox control strategies, including isolation of infectives, vaccination of healthcare workers, general vaccination, ring vaccination, and school closure	Compartmental epidemic model (Deterministic)	Smallpox	P	Local	P	M
Porco and others⁸⁵	To evaluate the effects of contact tracing and ring vaccination in controlling smallpox	Simulation (Discrete event, network of households and workplaces/social groups)	Smallpox	P, PH	Local (A community with households, workplaces, social groups)	P	M
Riley and Ferguson⁸⁶	To assess the efficacy of symptomatic case isolation, contact tracing with vaccination, and mass vaccination in controlling a smallpox outbreak	Simulation (Individual-based, incorporates spatial factors)	Smallpox	P	National (Great Britain)	P	M
Natural Disasters
Barbarosoğlu and Arda⁴⁸	To determine the most efficient flow of relief supplies in a transportation network in the aftermath of a rapid-onset disaster	Optimization (Stochastic programming)	Earthquake	P, FR	Local (Urban setting)	ND, T	L
Balcik and Beamon³⁹	To determine the number and location of global distribution centers for stockpiled relief items, as well as the quantity of those items to be maintained, in order to improve disaster response	Optimization (Linear and dynamic programming)	Earthquake	P	Global	ND, T	L
Fawcett and Oliveira¹⁵	To estimate the impact of health facility damage, rescue time, and out-of-region transportation on overall mortality from an earthquake	Simulation	Earthquake	H, P, PH, O	Local (Lisbon, Portugal)	T, Rx	L, M,O
Paul and others¹⁶	To estimate the transient patient surge at regional hospitals resulting from an earthquake	Simulation (Discrete event with regression-based parameters)	Earthquake	H, P, PH, O	Regional	ND, S, T, Rx	L, H
Regnier¹⁷	To determine, for specific locations in the United States, the relationship between hurricane track prediction accuracy and lead time for evacuations	Simulation (Markov model)	Hurricane	P	Local (Four U.S. coastal cities)	T	L, $
Özdamar, Ekinci, Küçükyazici⁴⁷	To apply vehicle routing and multi-commodity network flow techniques to develop an algorithm for efficiently dispatching relief supplies to a community affected by a rapid-onset disaster	Optimization	General natural disaster	P	Regional	T	L
Manmade Disasters
Beamon and Kotleba^{18, 19}	To develop inventory management models (order quantities and reorder points) to aid sustained humanitarian response to complex emergencies	Optimization (Simulation used to test model in a case study)	Conventional warfare	P	Regional	I	L
Papazoglou and Christou²¹	To determine the best short-term emergency response to a nuclear accident, considering the tradeoff between adverse health effects and costs	Optimization (Multiobjective)	Nuclear	P	Regional	P	M, $
Feng and Keller²²	To evaluate different plans for distribution of potassium iodide after release of radioactive iodine caused by a nuclear accident or terrorism	Optimization (Multiobjective decision analysis)	Nuclear	P, PH	Regional	D, I, P	O
Dombroski and Fischbeck^{49, 50}	To evaluate strategies (e.g., caring for patients at the bomb site vs. evacuation) for response to a “dirty bomb” (a conventional explosive wrapped in radioactive material)	Dispersion Model	Radiologic	FR, PH, O	Local	P, O	M, O
Georgopoulos and others⁵¹	To evaluate key parameters affecting the exposure of healthcare workers to hazardous materials from contaminated patients	Simulation	Chemical	H, P, FR	Local	P	M, O
Inoue, Yanagisawa, Kamae²⁰	To determine how to increase patient survival rates after a large-scale disaster through improvements in triage and transport procedures	Simulation	Airport accident	FR, PH	Local (Urban airport)	T, O	M
Christie and Levary⁴⁵	To develop a scenario planning tool for use in the event of a manmade rapid-onset disaster to effectively assign and transport patients for treatment	Simulation	Airplane crash in urban area	FR, PH	Local (Urban setting)	T, O	L
Hospital Planning
Levi and others^{27– 29, 53}	To evaluate Israeli hospitals’ disaster capacity and plans, train decision makers, and assist in managing real situations by identifying bottlenecks and evaluating a variety of response strategies	Simulation	Mass casualty events (e.g., conventional warfare)	H	Hospital	S, Rx, P, O	L, M, H
Kanter^{30, 31}	To evaluate tradeoffs in pediatric hospital strategies that involve altering the standard of care and increasing ICU surge capacity	Simulation	Mass casualty events	H	Hospital	O	H
Earnest and others³²	To predict the number of available isolation beds	Autoregressive moving average model	SARS	H	Hospital	O	H
Hupert and others³³	To estimate overcrowding of emergency departments due to adverse events from rapid mass prophylaxis campaigns	Spreadsheet model	Smallpox, Anthrax	PH	Hospital	P	M,H
Other Types of Models
Han and others⁴⁶	To determine efficient route and destination assignments for public evacuation after a large-scale disaster	Simulation	Large-scale disaster requiring evacuation of large urban area	P	Local (Urban setting)	T	L
Narzisi and others⁷³	To analyze hospital capacity, public health preparedness and response, and behavior of the public during a rapid-onset urban disaster	Simulation (Agent-based)	General disaster (distributed or point-source)	P	Local (Urban setting)	H	L
Dekle and others³⁸	To apply facility location techniques to identify potential disaster recovery centers for a local planning authority	Optimization (Integer programming)	General large-scale disaster	P	Local (County)	ND, T	L
Balcik, Beamon, and Smilowitz⁴²	To determine the allocation of relief supplies and scheduling and routing of vehicles for the “last mile” distribution of supplies in response to a disaster	Optimization (Mixed integer programming)	General disaster	P	Local	T, O	$, O
Barbarosoğlu, Özdamar, Çevik⁴³	To evaluate operational routing and loading decisions for helicopter dispatch during the aftermath of a disaster	Optimization (Mixed integer programming)	General disaster	P	Local (Urban setting)	T	L
Jotshi, Gong, Batta⁴⁴	To develop efficient emergency vehicle routing strategies in the aftermath of a major disaster using available real-time data	Optimization	General disaster	P	Local (Urban setting)	ND, T	H, L

^†

Disaster evaluated: Some of the included models apply to general disasters but for illustrative purposes used data from specific disaster types. Here, we have categorized them according to the specific type of disaster considered.

Decision makers considered: FR=first responders; H=hospital officials; P=planners (e.g., military planners, national-level emergency response planners); PH=public health officials; O=others (e.g., vaccine manufacturers)

^§

Decisions modeled: D=dispensing; I=inventory/stockpiling; ND=supply chain network design; P=prevention or mitigation of the disaster effects (e.g., vaccination strategies, quarantine, isolation, prophylaxis); Rx=treatment; S=healthcare workforce staffing; T=transportation; O=others (e.g., financing, traffic management)

^¶

Outcomes: $=costs; H=hospital utilization measures (e.g., bed capacity); L=logistical outcomes such as inventory levels or queue lengths; M=morbidity or mortality; QALY=quality-adjusted life years; O=others (e.g., probability of containment).