Multi-Agent Modeling of Biological and Chemical Threats

  • Kathleen M. Carley
  • Eric Malloy
  • Neal Altman
Chapter

Chapter Overview

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.

Keywords

BioWar Multi-agent simulation Pandemics Dynamic networks 

References

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Suggested Readings

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Online Resources Simulations of disease need to make use of existing disease descriptions and ontologies. These tend to be maintained by the military, the CDC and various professional societies:

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Kathleen M. Carley
    • 1
  • Eric Malloy
  • Neal Altman
  1. 1.CASOS — Center for Computational Analysis of Social and Organizational Systems, ISR — Institute for Software Research, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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