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Modeling dynamics of an influenza pandemic with heterogeneous coping behaviors: case study of a 2009 H1N1 outbreak in Arizona

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  • Wei Zhong

    (Renmin University of China)

  • Yushim Kim

    (Arizona State University)

  • Megan Jehn

    (Arizona State University)

Abstract

This paper aims to improve the accuracy of standard compartment models in modeling the dynamics of an influenza pandemic. Standard compartment models, which are commonly used in influenza simulations, make unrealistic assumptions about human behavioral responses during a pandemic outbreak. Existing simulation models with public avoidance also make a rigid assumption regarding the human behavioral response to influenza. This paper incorporates realistic assumptions regarding individuals’ avoidance behaviors in a standard compartment model. Both the standard and modified models are parameterized, implemented, and compared in the research context of the 2009 H1N1 influenza outbreak in Arizona. The modified model with heterogeneous coping behaviors forecasts influenza spread dynamics better than the standard model when evaluated against the empirical data, especially for the beginning of the 2009–2010 normal influenza season starting in October 2009 (i.e., the beginning of the second wave of 2009 H1N1). We end the paper with a discussion of the use of simulation models in efforts to help communities effectively prepare for and respond to influenza pandemics.

Suggested Citation

  • Wei Zhong & Yushim Kim & Megan Jehn, 2013. "Modeling dynamics of an influenza pandemic with heterogeneous coping behaviors: case study of a 2009 H1N1 outbreak in Arizona," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 622-645, December.
    • Handle: RePEc:spr:comaot:v:19:y:2013:i:4:d:10.1007_s10588-012-9146-6
    DOI: 10.1007/s10588-012-9146-6
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    1. Dennis S. Mileti & Colleen Fitzpatrick, 1992. "The Causal Sequence of Risk Communication in the Parkfield Earthquake Prediction Experiment," Risk Analysis, John Wiley & Sons, vol. 12(3), pages 393-400, September.
    2. Vaughan, E. & Tinker, T., 2009. "Effective health risk communication about pandemic influenza for vulnerable populations," American Journal of Public Health, American Public Health Association, vol. 99(S2), pages 324-332.
    3. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    4. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    5. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    6. 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.
    7. Byung-Kwang Yoo & Megumi Kasajima & Jay Bhattacharya, 2010. "Public Avoidance and the Epidemiology of novel H1N1 Influenza A," NBER Working Papers 15752, National Bureau of Economic Research, Inc.
    8. 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.
    9. A. J. Culyer & J. P. Newhouse (ed.), 2000. "Handbook of Health Economics," Handbook of Health Economics, Elsevier, edition 1, volume 1, number 1.
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    Cited by:

    1. Nicolò Gozzi & Daniela Perrotta & Daniela Paolotti & Nicola Perra, 2020. "Towards a data-driven characterization of behavioral changes induced by the seasonal flu," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-19, May.
    2. 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.

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