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Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea

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  • Yunhwan Kim
  • Ana Vivas Barber
  • Sunmi Lee

Abstract

Recurrent outbreaks of the influenza virus continue to pose a serious health threat all over the world. The role of mass media becomes increasingly important in modeling infectious disease transmission dynamics since it can provide public health information that influences risk perception and health behaviors. Motivated by the recent 2009 H1N1 influenza pandemic outbreak in South Korea, a mathematical model has been developed. In this work, a previous influenza transmission model is modified by incorporating two distinct media effect terms in the transmission rate function; (1) a theory-based media effect term is defined as a function of the number of infected people and its rage of change and (2) a data-based media effect term employs the real-world media coverage data during the same period of the 2009 influenza outbreak. The transmission rate and the media parameters are estimated through the least-squares fitting of the influenza model with two media effect terms to the 2009 H1N1 cumulative number of confirmed cases. The impacts of media effect terms are investigated in terms of incidence and cumulative incidence. Our results highlight that the theory-based and data-based media effect terms have almost the same influence on the influenza dynamics under the parameters obtained in this study. Numerical simulations suggest that the media can have a positive influence on influenza dynamics; more media coverage leads to a reduced peak size and final epidemic size of influenza.

Suggested Citation

  • Yunhwan Kim & Ana Vivas Barber & Sunmi Lee, 2020. "Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-21, June.
    • Handle: RePEc:plo:pone00:0232580
    DOI: 10.1371/journal.pone.0232580
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    References listed on IDEAS

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    1. Chris T Bauch & Samit Bhattacharyya, 2012. "Evolutionary Game Theory and Social Learning Can Determine How Vaccine Scares Unfold," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-12, April.
    2. Dantas, Eber & Tosin, Michel & Cunha Jr, Americo, 2018. "Calibration of a SEIR–SEI epidemic model to describe the Zika virus outbreak in Brazil," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 249-259.
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    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Swine Influenza (H1N1)

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    Cited by:

    1. Gilberto Gonzalez-Parra & Abraham J. Arenas, 2021. "Nonlinear Dynamics of the Introduction of a New SARS-CoV-2 Variant with Different Infectiousness," Mathematics, MDPI, vol. 9(13), pages 1-22, July.

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