Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society

Phillip Stroud (), Sara Del Valle (), Stephen Sydoriak (), Jane Riese () and Susan Mniszewski ()
Additional contact information
Phillip Stroud: http://public.lanl.gov/stroud
Sara Del Valle: http://cnls.lanl.gov/~sara/

Journal of Artificial Societies and Social Simulation, 2007, vol. 10, issue 4, 9

Abstract: 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.

Keywords: Agent Based Modeling; Computer Simulation; Epidemic Simulation; Public Health Policy (search for similar items in EconPapers)
Date: 2007-10-31
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2007-34-2

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