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A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak

Author

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  • Dionne M. Aleman

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Theodorus G. Wibisono

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Brian Schwartz

    (Ontario Agency for Health Protection and Promotion, Toronto, Ontario M5G 1V2, Canada; Department of Family and Community Medicine, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

Abstract

To effectively prepare for a pandemic disease outbreak, knowledge of how the disease will spread is paramount. The global outbreak of severe acute respiratory syndrome (SARS) in 2002--2003 highlighted the need for such data. This need is also apparent in preparing for and responding to all disease outbreaks, from pandemic influenza to avian flu. Many previous studies of disease make simplistic assumptions about transmission and infection rates and assume that each member of the population is identical or homogeneous. We propose an agent-based simulation model that treats each individual as unique, with nonhomogeneous transmission and infection rates correlated to demographic information and behavior. The results of the model are output to geographic information system software to provide a map of the estimated disease spread area, which can be used as a policy-making tool for determining a suitable mitigation strategy. The Ontario Agency for Health Protection and Promotion (OAHPP) uses the model for pandemic planning for the Greater Toronto area in Ontario, Canada.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:41:y:2011:i:3:p:301-315
    DOI: 10.1287/inte.1100.0550
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    References listed on IDEAS

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    1. 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.
    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. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
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    Cited by:

    1. Enayati, Shakiba & Özaltın, Osman Y., 2020. "Optimal influenza vaccine distribution with equity," European Journal of Operational Research, Elsevier, vol. 283(2), pages 714-725.
    2. Pengyi Shi & Jia Yan & Pinar Keskinocak & Andi L Shane & Julie L Swann, 2020. "The impact of opening dedicated clinics on disease transmission during an influenza pandemic," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-22, August.
    3. Ghaderi, Mohammad, 2022. "Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1016-1031.
    4. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "Literature review: The vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 268(1), pages 174-192.
    5. Jakob Heins & Jan Schoenfelder & Steffen Heider & Axel R. Heller & Jens O. Brunner, 2022. "A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy," Interfaces, INFORMS, vol. 52(6), pages 508-523, November.

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