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VIR-POX: An Agent-Based Analysis of Smallpox Preparedness and Response Policy

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Abstract

Because conjectural 'thought experiments' can be formalized, refined, and conducted systematically using computers, computational modeling is called for in situations that demand robust quantitative study of phenomena which occur only rarely, or may never occur at all. In light of mounting concerns regarding the threats of bioterrorism in general and smallpox in specific, we developed a stochastic agent-based model, VIR-POX, in order to explore the viability of available containment measures as defenses against the spread of this infectious disease. We found the various vaccination and containment programs to be highly interdependent, and ascertained that these policy options vary not only in their mean effects, but also in their subordination to factors of chance or otherwise uncontrollable interference, relationships which themselves fluctuate across ranges of the counterfactual distribution. Broadly speaking, ring vaccination rivaled mass vaccination if a very substantial proportion of smallpox cases could be detected and isolated almost immediately after infection, or if residual herd immunity in the population was relatively high. Pre-attack mass vaccination and post-attack mass vaccination were equivalent in their capacities to eliminate the virus from the population within five months, but the pre-attack strategy did so with significantly fewer deaths in the process. Our results suggest that the debate between ring and mass vaccination approaches may hinge on better understanding residual herd immunity and the feasibility of early detection measures.

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  • Benjamin M. Eidelson & Ian Lustick, 2004. "VIR-POX: An Agent-Based Analysis of Smallpox Preparedness and Response Policy," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(3), pages 1-6.
  • Handle: RePEc:jas:jasssj:2003-37-3
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    File URL: http://jasss.soc.surrey.ac.uk/7/3/6.html
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    Cited by:

    1. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    2. Phillip Stroud & Sara Del Valle & Stephen Sydoriak & Jane Riese & Susan Mniszewski, 2007. "Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-9.
    3. Irene Crimaldi & Pierre-Yves Louis & Ida Minelli, 2020. "Interacting non-linear reinforced stochastic processes: Synchronization and no-synchronization," Working Papers hal-02910341, HAL.

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