IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0232580.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232580
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0232580&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0232580?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    RePEc Biblio mentions

    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kabir, K.M. Ariful & Tanimoto, Jun, 2019. "Dynamical behaviors for vaccination can suppress infectious disease – A game theoretical approach," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 229-239.
    2. Ariful Kabir, K.M. & Tanimoto, Jun, 2021. "A cyclic epidemic vaccination model: Embedding the attitude of individuals toward vaccination into SVIS dynamics through social interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    3. Kulsum, Umma & Alam, Muntasir & Kamrujjaman, Md., 2024. "Modeling and investigating the dilemma of early and delayed vaccination driven by the dynamics of imitation and aspiration," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    4. Deka, Aniruddha & Bhattacharyya, Samit, 2022. "The effect of human vaccination behaviour on strain competition in an infectious disease: An imitation dynamic approach," Theoretical Population Biology, Elsevier, vol. 143(C), pages 62-76.
    5. Ullah, Mohammad Sharif & Higazy, M. & Kabir, K.M. Ariful, 2022. "Dynamic analysis of mean-field and fractional-order epidemic vaccination strategies by evolutionary game approach," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    6. Kejriwal, Saransh & Sheth, Sarjan & Silpa, P.S. & Sarkar, Sumit & Guha, Apratim, 2022. "Attaining herd immunity to a new infectious disease through multi-stage policies incentivising voluntary vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    7. Mendonça, J.P. & Brum, Arthur A. & Lyra, M.L. & Lira, Sérgio A., 2024. "Evolutionary game dynamics and the phase portrait diversity in a pandemic scenario," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    8. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    9. Yu, Zhenhua & Zhang, Jingmeng & Zhang, Yun & Cong, Xuya & Li, Xiaobo & Mostafa, Almetwally M., 2024. "Mathematical modeling and simulation for COVID-19 with mutant and quarantined strategy," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    10. Ge, Jingwen & Wang, Wendi, 2022. "Vaccination games in prevention of infectious diseases with application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    11. Ichinose, Genki & Kurisaku, Takehiro, 2017. "Positive and negative effects of social impact on evolutionary vaccination game in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 84-90.
    12. Oestereich, André L. & Pires, Marcelo A. & Crokidakis, Nuno & Cajueiro, Daniel O., 2023. "Optimal rewiring in adaptive networks in multi-coupled vaccination, epidemic and opinion dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    13. Meng, Xueyu & Lin, Jianhong & Fan, Yufei & Gao, Fujuan & Fenoaltea, Enrico Maria & Cai, Zhiqiang & Si, Shubin, 2023. "Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    14. Jabili Angina & Anish Bachhu & Eesha Talati & Rishi Talati & Jan Rychtář & Dewey Taylor, 2022. "Game-Theoretical Model of the Voluntary Use of Insect Repellents to Prevent Zika Fever," Dynamic Games and Applications, Springer, vol. 12(1), pages 133-146, March.
    15. Chang, Sheryl L. & Piraveenan, Mahendra & Prokopenko, Mikhail, 2020. "Impact of network assortativity on epidemic and vaccination behaviour," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    16. Hair, Nicole L. & Gruber, Anja & Urban, Carly, 2021. "Personal belief exemptions for school-entry vaccinations, vaccination rates, and academic achievement," Journal of Health Economics, Elsevier, vol. 78(C).
    17. Benyun Shi & Guangliang Liu & Hongjun Qiu & Yu-Wang Chen & Shaoliang Peng, 2019. "Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks," Complexity, Hindawi, vol. 2019, pages 1-13, February.
    18. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    19. Yanling Zhang & Feng Fu, 2018. "Strategy intervention for the evolution of fairness," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-13, May.
    20. Okita, Kouki & Tatsukawa, Yuichi & Utsumi, Shinobu & Arefin, Md. Rajib & Hossain, Md. Anowar & Tanimoto, Jun, 2023. "Stochastic resonance effect observed in a vaccination game with effectiveness framework obeying the SIR process on a scale-free network," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0232580. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.