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A Hybrid Model for Disease Spread and an Application to the SARS Pandemic

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Author Info

  • Teruhiko Yoneyama

    ()

  • Sanmay Das

    ()

  • Mukkai Krishnamoorthy

    ()

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    Abstract

    Pandemics can cause immense disruption and damage to communities and societies. Thus far, modeling of pandemics has focused on either large-scale differential equation models like the SIR and the SEIR models, or detailed micro-level simulations, which are harder to apply at a global scale. This paper introduces a hybrid model for pandemics that considers both global and local spread of infection. We hypothesize that the spread of an infectious disease between regions is significantly influenced by global traffic patterns and that the spread within a region is influenced by local conditions. Thus we model the spread of pandemics considering the connections between regions for the global spread of infection and population density based on the SEIR model for the local spread of infection. We validate our hybrid model by carrying out a simulation study for the spread of the SARS pandemic of 2002-2003 using available data on population, population density, and traffic networks between different regions. While it is well-known that international relationships and global traffic patterns significantly influence the spread of pandemics, our results show that integrating these factors into relatively simple models can greatly improve the results of modeling disease spread.

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    File URL: http://jasss.soc.surrey.ac.uk/15/1/5/5.pdf
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    Bibliographic Info

    Article provided by Journal of Artificial Societies and Social Simulation in its journal Journal of Artificial Societies and Social Simulation.

    Volume (Year): 15 (2012)
    Issue (Month): 1 ()
    Pages: 5

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    Handle: RePEc:jas:jasssj:2010-96-2

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    Related research

    Keywords: Data-Driven Simulation; Epidemiology; Network-Based Simulation; SARS;

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