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Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society

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EpiSimS is a massive simulation of the movements, activities, and social interactions of individuals in realistic synthetic populations, and of the dynamics of contagious disease spread on the resulting social contact network. This paper describes the assumptions and methodology in the EpiSimS model. It also describes and presents a simulation of the spatial dynamics of pandemic influenza in an artificial society constructed to match the demographics of southern California. As an example of the utility of the massive simulation approach, we demonstrate a strong correlation between local demographic characteristics and pandemic severity, which gives rise to previously unanticipated spatial pandemic hotspots. In particular, the average household size in a census tract is strongly correlated with the clinical attack rate computed by the simulation. Public heath agencies with responsibility for communities having relatively high population per household should expect to be more severely hit by a pandemic.

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  • 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.
    • Handle: RePEc:jas:jasssj:2007-34-2
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    1. 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.
    2. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    3. Jill Bigley Dunham, 2005. "An Agent-Based Spatially Explicit Epidemiological Model in MASON," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-3.
    4. Chung-Yuan Huang & Chuen-Tsai Sun & Ji-Lung Hsieh & Holin Lin, 2004. "Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(4), pages 1-2.
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    1. > Economics of Welfare > Health Economics > Economics of Pandemics

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    1. S. M. Mniszewski & S. Y. Del Valle & P. D. Stroud & J. M. Riese & S. J. Sydoriak, 2008. "Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 209-221, September.
    2. Dionne M. Aleman & Theodorus G. Wibisono & Brian Schwartz, 2011. "A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak," Interfaces, INFORMS, vol. 41(3), pages 301-315, June.
    3. Myong-Hun Chang & Troy Tassier, 2021. "Spatially Heterogeneous Vaccine Coverage and Externalities in a Computational Model of Epidemics," Computational Economics, Springer;Society for Computational Economics, vol. 58(1), pages 27-55, June.
    4. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    5. Geoffrey Fairchild & Kyle S. Hickmann & Susan M. Mniszewski & Sara Y. Del Valle & James M. Hyman, 2014. "Optimizing human activity patterns using global sensitivity analysis," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 394-416, December.
    6. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.
    7. Kyle S Hickmann & Geoffrey Fairchild & Reid Priedhorsky & Nicholas Generous & James M Hyman & Alina Deshpande & Sara Y Del Valle, 2015. "Forecasting the 2013–2014 Influenza Season Using Wikipedia," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-29, May.

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