Multi-Agent Modeling of Biological and Chemical Threats
When pandemics, chemical spills, and bio-warfare attacks occur cities must respond quickly to mitigate loss of life. Which interventions should be used? How can we assess intervention policies for novel and low frequency events? Reasoning about such events is difficult for people due to the high level of complexity and the multitude of interacting factors. Computational models, however, are a particularly useful tool for reasoning about complex systems. In this paper, we describe a multi-agent dynamic-network model and demonstrate its use for policy assessment. BioWar is a city-level multi-agent dynamic-network model of the impact of epidemiological events on a city’s population. Herein, we describe BioWar and then use it to examine the impact of school closures and quarantine on the spread and impact of pandemic influenza. Key aspects of the model include utilization of census data to set population characteristics, imputed social networks among agents, and flexible disease modeling at the symptom level. This research demonstrates that high-fidelity models can be effectively used to assess policies.
KeywordsBioWar Multi-agent simulation Pandemics Dynamic networks
- Begier, E.M., Sockwell, D., Branch, L.M., Davies-Cole, J.O., Jones, L.H., Edwards, L., Casani, J.A., and Blythe, D. (2003). “The National Capitol Region’s Emergency Department Syndromic Surveillance System: Do Chief Complaints and Discharge Diagnosis Yield Different Results?,” Emerging Infectious Diseases, 9(3), 393–396.PubMedCrossRefGoogle Scholar
- Burton, R.M. (1995). “Validation and Docking: An Overview, Summary and Challenge,” in Simulating Organizations: Computational Models of Institutions and Groups. MIT Press: Cambridge, MA, 215–228.Google Scholar
- Carley, K.M. (2009). “Computational Modeling for Reasoning About the Social Behavior of Humans,” Computational, Mathematical and Organization Theory, 15(1), 47–59. Available: http://springerlink.com/content/k44jr16031412578/, Retrieved: 4/2009
- Carley, K.M. (1996). “Validating Computational Models,” Working Paper.Google Scholar
- GSS – General Social Survey (2009). http://www.norc.org/GSS+Website/, Retrieved: 4/2009
- Isada, C.M., Kasten, B.L., Goldman, M.P., Gray, L.D., and Aberg, J.A. (2003). Infectious Disease Handbook, AACC.Google Scholar
- Maxwell D. and Carley K.M. (2009). “Principles for Effectively Representing Heterogeneous Populations in Multi-Agent Simulations,” in Complex Systems in Knowledge Based Environments, Tolk, A. (ed.), Ch. 8, 199–228, Springer–Verlag.Google Scholar
- Perkins, B.A., Popovic, T., and Yeskey, K. (eds.) (2002). “Bioterrorism-Related Anthrax,” Emerging Infectious Diseases, 8(10) (special edition). http://www.cdc.gov/ncidod/EID/vol8no10/contents_v8n10.htm, Retrieved: 3/2008.
- USAMRIID – US Army Medical Research Institute for Infectious Diseases (2001). USAMRIID’s Medical Management of Biological Casualties Handbook.Google Scholar
- U.S. Census Bureau (2008). Metropolitan and Micropolitan Statistical Areas. http://www.census.gov/population/www/estimates/metroarea.html, Retrieved: 3/2008.
- U.S. Census Bureau (1994). Geographic Areas Reference Manual, Available: http://www.census.gov/geo/www/garm.html, Retrieved: 3/2008.
- West, K. H. (2001). Infectious Disease Handbook for Emergency Care Personnel, ACGIH.Google Scholar
- Keeling, M.J. and Rohani, P. (2007). Modeling Infectious Diseases in Humans and Animals, Princeton University Press: Princeton, NJ.Google Scholar
- Provides a comprehensive introduction to infectious disease modeling and has an associated web site. R and C++ models.Google Scholar
- Epstein, J.M. and Axtell, R. (1996). Growing Artificial Societies: Social Science From the Bottom Up. MIT Press/Brookings Institution: Cambridge, MA.Google Scholar
- Early multi-agent simulation system showing the power of bottom-up reasoning even when using highly simplistic models.Google Scholar
- Provides a good general introduction to multi-agent simulations.Google Scholar