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A Novel Approach to Multihazard Modeling and Simulation

Published online by Cambridge University Press:  08 April 2013

Abstract

Objective: To develop and apply a novel modeling approach to support medical and public health disaster planning and response using a sarin release scenario in a metropolitan environment.

Methods: An agent-based disaster simulation model was developed incorporating the principles of dose response, surge response, and psychosocial characteristics superimposed on topographically accurate geographic information system architecture. The modeling scenarios involved passive and active releases of sarin in multiple transportation hubs in a metropolitan city. Parameters evaluated included emergency medical services, hospital surge capacity (including implementation of disaster plan), and behavioral and psychosocial characteristics of the victims.

Results: In passive sarin release scenarios of 5 to 15 L, mortality increased nonlinearly from 0.13% to 8.69%, reaching 55.4% with active dispersion, reflecting higher initial doses. Cumulative mortality rates from releases in 1 to 3 major transportation hubs similarly increased nonlinearly as a function of dose and systemic stress. The increase in mortality rate was most pronounced in the 80% to 100% emergency department occupancy range, analogous to the previously observed queuing phenomenon. Effective implementation of hospital disaster plans decreased mortality and injury severity. Decreasing ambulance response time and increasing available responding units reduced mortality among potentially salvageable patients. Adverse psychosocial characteristics (excess worry and low compliance) increased demands on health care resources. Transfer to alternative urban sites was possible.

Conclusions: An agent-based modeling approach provides a mechanism to assess complex individual and systemwide effects in rare events. (Disaster Med Public Health Preparedness. 2009;3:75–87)

Type
Original Research and Critical Analysis
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2009

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References

REFERENCES

1.Lerner, EB, Schwartz, RB, Coule, PL, et alMass casualty triage: An evaluation of the data and development of a proposed national guideline. Disaster Med Public Health Preparedness. 2008;2 Suppl 1S25S34.CrossRefGoogle ScholarPubMed
2.Dausey, DJ, Buehler, JW, Lurie, N.Designing and conducting tabletop exercises to assess public health preparedness for manmade and naturally occurring biological threats. BMC Public Health. 2007;7:92.Google Scholar
3.Hoard, M, Homer, J, Manley, W, et alSystems modeling in support of evidence-based disaster planning for rural areas. Int J Hyg Environ Health. 2005;208:117125.Google Scholar
4.Burstein, JL.The myths of disaster education. Ann Emerg Med. 2006;47:5052.Google Scholar
5.Auf der Heide, E.The importance of evidence-based disaster planning. Ann Emerg Med. 2006;47:3449.CrossRefGoogle ScholarPubMed
6.Committee on the Future of Emergency Care in the United States Health System, Board on Health Care Services, Institute of Medicine of the National Academies. Future of Emergency Care Series: Hospital-based Emergency Care: At the Breaking Point. Washington, DC: National Academies of Science; 2006.Google Scholar
7.Defense Threat Reduction Agency (DTRA). Consequence assessment tool set (CATS) [computer program]. Version 6.0. Fort Belvoir, VA: Defense Threat Reduction Agency; 2005.Google Scholar
8.Burke, DS, Epstein, JM, Cummings, DA, et alIndividual-based computational modeling of smallpox epidemic control strategies. Acad Emerg Med. 2006;13:11421149.Google ScholarPubMed
9. Hupert N, Cuomo J. BERM (Bioterrorism and Epidemic Outbreak Response Model) [computer program]. Version 2.0. Rockville, MD: AHRQ; 2004. http://www.ahrq.gov/research/biomodel.htm. Accessed March 14, 2009.Google Scholar
10. Johns Hopkins Office of Critical Event Preparedness and Response (CEPAR) and Johns Hopkins University Applied Physics Laboratory (JHU/APL). Electronic mass casualty assessment & planning scenarios (EMCAPS) [computer program]. Version 1.0, Baltimore, MD: CEPAR; 2006. http://www.hopkins-cepar.org/EMCAPS/EMCAPS.html. Accessed March 14, 2009.Google Scholar
11.Bostick, NA, Subbarao, I, Burkle, FM Jr, et alDisaster triage systems for large-scale catastrophic events. Disaster Med Public Health Preparedness. 2008;2 (Suppl 1)S35S39.Google Scholar
12. Carley KM, Altman N, Kaminsky B, et al. BioWar: A city-scale multi-agent network model of weaponized biological attacks. Center for Computational Analysis of Social and Organizational Systems Technical Report CMU-ISRI-04-101. Pittsburgh: CASOS, Carnegie Mellon University; 2004. http://reports-archive.adm.cs.cmu.edu/anon/isri2004/CMU-ISRI-04-101.pdf. Accessed March 14, 2009.Google Scholar
13.Balasubramanian, V, Massaguer, D, Mehrotra, S, et alDrillSim: a simulation framework for emergency response drills. Lecture Notes in Computer Science. 2006;3975:237248.Google Scholar
14. Schwehm M, Leary C, Duerr HP, et al. InterSim: a network-based outbreak investigation and intervention planning tool [abstract 219]. In: Minai A, Braha D, Bar-Yam Y, eds. Proceedings of the Sixth International Conference on Complex Systems. 2006. http://necsi.org/events/iccs6/proceedings.html. Accessed April 17, 2009.Google Scholar
15.Eubank, S, Guclu, H, Kumar, VS, et alModelling disease outbreaks in realistic urban social networks. Nature. 2004;429:180184.Google Scholar
16.Berry, BJ, Kiel, LD, Elliott, E.Adaptive agents, intelligence, and emergent human organization: Capturing complexity through agent-based modeling. Proc Natl Acad Sci U S A. 2002;99 (Suppl 3)71877188.Google Scholar
17.Dugatkin, LA, Dugatkin, AD, Atlas, RM, Perlin, MH.Cheating on the edge. PLoS ONE. 2008;3:e2763.Google Scholar
18.Pappalardo, F, Musumeci, S, Motta, S.Modeling immune system control of atherogenesis. Bioinformatics. 2008;24:17151721.Google Scholar
19.Rabin, R, de Charro, F.EQ-5D: a measure of health status from the EuroQol group. Ann Med. 2001;33:337343.Google Scholar
20.Horsman, J, Furlong, W, Feeny, D, et alThe health utilities index (HUI®): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003;1:54.Google Scholar
21.Felson, M, Belanger, ME, Bichler, GM, et al Redesigning hell: preventing crime and disorder at the port authority bus terminal.Clarke RV. Preventing Mass Transit Crime, Crime Prevention Studies Series. Vol 6. New York: Criminal Justice Press; 1996.Google Scholar
22. New York City Department of City Planning. Primary land use tax lot output (PLUTO™) (C) 2003–2007. http://www.nyc.gov/html/dcp/html/bytes/applbyte.shtml. Accessed September 24, 2007.Google Scholar
23. The City Of New York Department of Information Technology and Telecommunications (DoITT). NYCMAP ONLINE. http://www.nyc.gov/html/doitt/html/eservices/eservices_gis_downloads.shtml. Accessed September 24, 2007.Google Scholar
24.Kukkonen, J, Riikonen, K, Nikmo, J, et alModelling aerosol processes related to the atmospheric dispersion of sarin. J Hazard Mater. 2001;85:165179.Google Scholar
25.Subcommittee on Acute Exposure Guideline Levels, Committee on Toxicology, National Research Council. Acute Exposure Guideline Levels for Specific Airborne Chemicals. Vol 3. Washington, DC: National Academies Press; 2003.Google Scholar
26.Watson, A, Opresko, D, Young, R, et alDevelopment and application of acute exposure guideline levels (AEGLs) for chemical warfare nerve and sulfur mustard agents. J Toxicol Environ Health B Crit Rev. 2006;9:173263.CrossRefGoogle ScholarPubMed
27. US Environmental Protection Agency, Indoor Environment Management Branch. SPILL.EXE, IAQX (simulation tool kit for indoor air quality and inhalation exposure) [computer program]. Version 1.0. Research Triangle Park, NC: U.S. Environmental Protection Agency, Indoor Environment Management Branch; 2007. http://www.epa.gov/appcdwww/iemb/model.htm. Accessed March 19, 2009.Google Scholar
28.Okumura, T, Suzuki, K, Fukuda, A, et alThe Tokyo subway sarin attack: disaster management: 1. Community emergency response. Acad Emerg Med. 1998;5:613617.Google Scholar
29.Committee on Confronting Terrorism in Russia, Office for Central Europe and Eurasia Development, Security, and Cooperation, National Research Council in Cooperation with the Russian Academy of Sciences. High Impact Terrorism: Proceedings of a Russian-American Workshop. Washington, DC: National Academies Press; 2002.Google Scholar
30.Davis, SC, McHenry, KE.A retrospective analysis of mass casualty presentation resulting from the release of toxic chemicals. Int J Emerg Manag (Switzerland). 2005;2:231238.CrossRefGoogle Scholar
31.US Department of Homeland Security. National Planning Scenarios: Created for Use in National, Federal, State, and Local Homeland Security Preparedness Activities. Washington, DC: US Department of Homeland Security; 2005.Google Scholar
32.Committee on Toxicology, National Research Council. Review of Acute Human-Toxicity Estimates for Selected Chemical-Warfare Agents. Washington, DC: National Academies Press; 1997.Google Scholar
33. Grotte JH, Yang LI. Report of the Workshop on Chemical Agent Toxicity for Acute Effects, May 11–12, 1998. IDA Document D-2176. Alexandria, VA: Institute for Defense Analyses; 2001. https://usachppm.apgea.army.mil/chemicalagent/caw/IDAreport(2001).pdf. Accessed March 19, 2009.Google Scholar
34. US Army Center for Health Promotion and Preventive Medicine. Acute Toxicity Estimation and Operational Risk Management of Chemical Warfare Agents. USACHPPM Report no. 47-EM-5863-04. Aberdeen Proving Ground, MD: US Army Center for Health Promotion and Preventive Medicine; 2004. http://chppm-www.apgea.army.mil/chemicalagent/PDFFiles/AcuteCWToxValuesandOperationalRiskManagement_CHPPM2004_5.pdf. Accessed March 19, 2009.Google Scholar
35. Sorensen JH, Watson AP, Vogt BM, et al. The Reutter/Wade toxicity report and CSEPP civilian emergency planning. ORNL/TM-1999/154. Oak Ridge, TN: Oak Ridge National Laboratory; 1999. http://www.ornl.gov/webworks/cpr/rpt/104167.pdf. Accessed March 19, 2009.Google Scholar
36.Bide, RW, Armour, SJ, Yee, E.GB toxicity reassessed using newer techniques for estimation of human toxicity from animal inhalation toxicity data: new method for estimating acute human toxicity (GB). J Appl Toxicol. 2005;25:393409.Google Scholar
37. Crosier RB, Sommerville DR. Relationship Between Toxicity Values for the Military Population and Toxicity Values for the General Population. ECBC-TR-224. Aberdeen Proving Ground, MD: U.S. Army Edgewood Chemical Biological Center; 2002:1–40. http://handle.dtic.mil/100.2/ADA482895. Accessed March 19, 2009.Google Scholar
38. Sommerville DR. Relationship between the Dose-Response Curves for Lethality and Severe Effects for Chemical Warfare Nerve Agents. DTIC Research Report ADA448899. Aberdeen Proving Ground, MD: U.S. Army Edgewood Chemical Biological Center; 2005: 1-11. http://handle.dtic.mil/100.2/ADA448899. Accessed March 19, 2009.Google Scholar
39.Sacco, WJ, Navin, DM, Fiedler, KE, et alPrecise formulation and evidence-based application of resource-constrained triage. Acad Emerg Med. 2005;12:759770.Google Scholar
40.Markel, G, Krivoy, A, Rotman, E, et alMedical management of toxicological mass casualty events. Isr Med Assoc J. 2008;10:761766.Google Scholar
41.Okumura, T, Suzuki, K, Fukuda, A, et alThe Tokyo subway sarin attack: disaster management: 2. Hospital response. Acad Emerg Med. 1998;5:618624.Google Scholar
42. Department of Homeland Security, US Fire Administration, National Fire Data Center. The World Trade Center Bombing: Report and analysis. USFA-TR-076. Emmitsburg, MD: US Fire Administration; 1993:1–154. http://www.usfa.dhs.gov/downloads/pdf/publications/tr-076.pdf. Accessed March 19, 2009.Google Scholar
43.Centers for Disease Control and Prevention. Rapid assessment of injuries among survivors of the terrorist attack on the world trade center—New York City, September 2001. MMWR Morb Mortal Wkly Rep. 2002;51:15.Google Scholar
44.Hildebrand, S, Bleetman, A.Comparative study illustrating difficulties educating the public to respond to chemical terrorism. Prehosp Disaster Med. 2007;22:3541.Google Scholar
45.Agency for Healthcare Research and Quality, US Department of Health and Human Services. National Hospital Available Beds for Emergencies and Disasters (HAvBED) System: Final Report. AHRQ Publication No. 05-0103. Rockville, MD: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services; 2005.Google Scholar
46.Asplin, BR, Flottemesch, TJ, Gordon, BD.Developing models for patient flow and daily surge capacity research. Acad Emerg Med. 2006;13:11091113.CrossRefGoogle ScholarPubMed
47.Hoot, NR, Zhou, C, Jones, I, et alMeasuring and forecasting emergency department crowding in real time. Ann Emerg Med. 2007;49:747755.Google Scholar
48.Husk, G, Waxman, DA.Using data from hospital information systems to improve emergency department care. Acad Emerg Med. 2004;11:12371244.Google Scholar
49.Green, LV, Soares, J, Giglio, JF, et alUsing queueing theory to increase the effectiveness of emergency department provider staffing. Acad Emerg Med. 2006;13:6168.Google Scholar
50.McCarthy, ML, Zeger, SL, Ding, R, et alThe challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15:337346.CrossRefGoogle ScholarPubMed
51.Kanter, RK, Moran, JR.Hospital emergency surge capacity: an empiric New York statewide study. Ann Emerg Med. 2007;50:314319.Google Scholar
52.DeLia, D.Annual bed statistics give a misleading picture of hospital surge capacity. Ann Emerg Med. 2006;48:384388.Google Scholar
53.McManus, ML, Long, MC, Cooper, A, et alQueuing theory accurately models the need for critical care resources. Anesthesiology. 2004;100:12711276.Google Scholar
54.McCaig, LF, Nawar, EW.National Hospital Ambulatory Medical Care Survey: 2004 Emergency Department Summary. Advance Data From Vital and Health Statistics. Report no. 372. Hyattsville, MD: National Center for Health Statistics; 2006.Google Scholar
55.Brown, DW, Young, SL, Engelgau, MM, et alEvidence-based approach for disaster preparedness authorities to inform the contents of repositories for prescription medications for chronic disease management and control. Prehosp Disaster Med. 2008;23:447457.CrossRefGoogle ScholarPubMed
56.Hick, JL, Hanfling, D, Burstein, JL, et alHealth care facility and community strategies for patient care surge capacity. Ann Emerg Med. 2004;44:253261.Google Scholar
57.Halpern, P, Tsai, MC, Arnold, JL, et alMass-casualty, terrorist bombings: Implications for emergency department and hospital emergency response: II. Prehosp Disaster Med. 2003;18:235241.CrossRefGoogle Scholar
58.Rubinson, L, Hick, JL, Curtis, JR, et alDefinitive care for the critically ill during a disaster: medical resources for surge capacity. From a task force for mass critical care summit meeting, January 26–27, 2007, Chicago, IL. Chest. 2008;133:32S50S.Google Scholar
59.Narzisi, G, Mysore, V, Mishra, B.Multi-objective evolutionary optimization of agent based models: an application to emergency response planning. CI (Computational Intelligence). 2006;523:224230.Google Scholar
60.Barnett, DJ, Everly, GS Jr, Parker, CL, et alApplying educational gaming to public health workforce emergency preparedness. Am J Prev Med. 2005;28:390395.Google Scholar
61.Kelen, GD, McCarthy, ML.The science of surge. Acad Emerg Med. 2006;13:10891094.Google Scholar
62.Saunders, CE.Time study of patient movement through the emergency department: sources of delay in relation to patient acuity. Ann Emerg Med. 1987;16:12441248.Google Scholar
63.Ball, CG, Kirkpatrick, AW, Mulloy, RH, et alThe impact of multiple casualty incidents on clinical outcomes. J Trauma. 2006;61:10361039.CrossRefGoogle ScholarPubMed
64.Forster, AJ, Stiell, I, Wells, G, et alThe effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10:127133.Google Scholar
65.Fatovich, DM, Hirsch, RL.Entry overload, emergency department overcrowding, and ambulance bypass. Emerg Med J. 2003;20:406409.CrossRefGoogle ScholarPubMed
66.Avidan, V, Hersch, M, Spira, RM, et alCivilian hospital response to a mass casualty event: the role of the intensive care unit. J Trauma. 2007;62:12341239.Google Scholar
67.Yanagisawa, N, Morita, H, Nakajima, T.Sarin experiences in Japan: acute toxicity and long-term effects. J Neurol Sci. 2006;249:7685.CrossRefGoogle ScholarPubMed
68.Arnold, JL, Tsai, MC, Halpern, P, et alMass-casualty, terrorist bombings: epidemiological outcomes, resource utilization, and time course of emergency needs: I. Prehosp Disaster Med. 2003;18:220234.Google Scholar
69.Feeney, JM, Goldberg, R, Blumenthal, JA, et alSeptember 11, 2001, revisited: a review of the data. Arch Surg. 2005;140:10681073.Google Scholar
70.Dombroski, M, Fischhoff, B, Fischbeck, P.Predicting emergency evacuation and sheltering behavior: a structured analytical approach. Risk Anal. 2006;26:16751688.Google Scholar
71.Williams, BL, Magsumbol, MS.Emergency preparedness among people living near US Army chemical weapons sites after September 11, 2001. Am J Public Health. 2007;97:16011606.Google Scholar
72.Lewis, RJ.Modeling complex systems: gaining valid insights and avoiding mathematical delusions. Acad Emerg Med. 2007;14:795798.Google Scholar
73.Gruss, E.A correction for primary blast injury criteria. J Trauma. 2006;60:12841289.CrossRefGoogle ScholarPubMed
74.Medical Consequences of Nuclear Warfare. Falls Church, VA: Office of the Surgeon General; 1989.Google Scholar
75.Wilkening, DA.Sverdlovsk revisited: modeling human inhalation anthrax. Proc Natl Acad Sci U S A. 2006;103:75897594.Google Scholar
76.Luo, N, Johnson, JA, Shaw, JW, et alSelf-reported health status of the general adult U.S. population as assessed by the EQ-5D and health utilities index. Med Care. 2005;43:10781086.Google Scholar
77.Stiell, IG, Nesbitt, LP, Nichol, G, et alComparison of the cerebral performance category score and the health utilities index for survivors of cardiac arrest. Ann Emerg Med. 2009;53:241248.CrossRefGoogle ScholarPubMed
78.Cohen, SB.Design strategies and innovations in the medical expenditure panel survey. Med Care. 2003;41:III5III12.Google Scholar
79. Stenner RD, Hadley DL, Armstrong PR, et al. Indoor Air Nuclear, Biological, and Chemical Health Modeling and Assessment System. PNNL-13435. Richland, WA: Pacific Northwest National Laboratory; 2001. http://www.pnl.gov/main/publications/external/technical_reports/PNNL-13435.pdf. Accessed March 19, 2009.Google Scholar
80.Pangi, R.Consequence Management in the 1995 Sarin Attacks on the Japanese Subway System. BCSIA Discussion Paper 2002–4, ESDP Discussion Paper ESDP-2002-01. Boston: John F. Kennedy School of Government, Harvard University; 2002:141.Google Scholar
81.Jones, GT.Agent-based modeling: use with necessary caution. Am J Public Health. 2007;97:780781.Google Scholar
82.Wiinamaki, A, Dronzek, R. Using simulation in the architectural concept phase of an emergency department design.Chick S, Sánchez PJ, Ferrin D, et al, eds. Proceedings of the 2003 Winter Simulation Conference, Vol 2. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2003:19121916.Google Scholar