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Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior

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  • Wei Zhong

    (Renmin University of China)

Abstract

Individual responsive behavior to an influenza pandemic has significant impacts on the spread dynamics of this epidemic. Current influenza modeling efforts considering responsive behavior either oversimplify the process and may underestimate pandemic impacts, or make other problematic assumptions and are therefore constrained in utility. This study develops an agent-based model for pandemic simulation, and incorporates individual responsive behavior in the model based on public risk communication literature. The resultant model captures the stochastic nature of epidemic spread process, and constructs a realistic picture of individual reaction process and responsive behavior to pandemic situations. The model is then applied to simulate the spread dynamics of 2009 H1N1 influenza in a medium-size community in Arizona. Simulation results illustrate and compare the spread timeline and scale of this pandemic influenza, without and with the presence of pubic risk communication and individual responsive behavior. Sensitivity analysis sheds some lights on the influence of different communication strategies on pandemic impacts. Those findings contribute to effective pandemic planning and containment, particularly at the beginning of an outbreak.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:comaot:v:23:y:2017:i:4:d:10.1007_s10588-016-9238-9
    DOI: 10.1007/s10588-016-9238-9
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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Tang, C.S.K. & Wong, C.-Y., 2003. "An Outbreak of the Severe Acute Respiratory Syndrome: Predictors of Health Behaviors and Effect of Community Prevention Measures in Hong Kong, China," American Journal of Public Health, American Public Health Association, vol. 93(11), pages 1887-1889.
    3. Philipson, Tomas, 2000. "Economic epidemiology and infectious diseases," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 33, pages 1761-1799, Elsevier.
    4. 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.
    5. 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.
    6. 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.
    7. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    8. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    9. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
    10. 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.
    11. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    12. 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.
    13. 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.
    14. 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.
    15. Ling Bian, 2004. "A Conceptual Framework for an Individual-Based Spatially Explicit Epidemiological Model," Environment and Planning B, , vol. 31(3), pages 381-395, June.
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