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The coevolution of the spread of a disease and competing opinions in multiplex networks

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  • Fang, Fanshu
  • Ma, Jing
  • Li, Yanli

Abstract

The COVID-19 pandemic has resulted in a proliferation of conflicting opinions on physical distancing across various media platforms, which has had a significant impact on human behavior and the transmission dynamics of the disease. Inspired by this social phenomenon, we present a novel UAP-SIS model to study the interaction between conflicting opinions and epidemic spreading in multiplex networks, in which individual behavior is based on diverse opinions. We distinguish susceptibility and infectivity among individuals who are unaware, pro-physical distancing and anti-physical distancing, and we incorporate three kinds of mechanisms for generating individual awareness. The coupled dynamics are analyzed in terms of a microscopic Markov chain approach that encompasses the aforementioned elements. With this model, we derive the epidemic threshold which is related to the diffusion of competing opinions and their coupling configuration. Our findings demonstrate that the transmission of the disease is shaped in a significant manner by conflicting opinions, due to the complex interaction between such opinions and the disease itself. Furthermore, the implementation of awareness-generating mechanisms can help to mitigate the overall prevalence of the epidemic, and global awareness and self-awareness can be interchangeable in certain instances. To effectively curb the spread of epidemics, policymakers should take steps to regulate social media and promote physical distancing as the mainstream opinion.

Suggested Citation

  • Fang, Fanshu & Ma, Jing & Li, Yanli, 2023. "The coevolution of the spread of a disease and competing opinions in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:chsofr:v:170:y:2023:i:c:s0960077923002771
    DOI: 10.1016/j.chaos.2023.113376
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    References listed on IDEAS

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    1. Zhan, Xiu-Xiu & Liu, Chuang & Sun, Gui-Quan & Zhang, Zi-Ke, 2018. "Epidemic dynamics on information-driven adaptive networks," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 196-204.
    2. Nicola Bellomo & Richard Bingham & Mark A.J. Chaplain & Giovanni Dosi & Guido Forni & Damian A. Knopoff & John Lowengrub & Reidun Twarock & Maria Enrica Virgillito, 2020. "A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world," LEM Papers Series 2020/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Daniel J McGrail & Jianli Dai & Kathleen M McAndrews & Raghu Kalluri, 2020. "Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-9, July.
    4. Quantong Guo & Yanjun Lei & Chengyi Xia & Lu Guo & Xin Jiang & Zhiming Zheng, 2016. "The Role of Node Heterogeneity in the Coupled Spreading of Epidemics and Awareness," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.
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    Cited by:

    1. Zhiyong Hong & Huiyu Zhou & Zhishuang Wang & Qian Yin & Jingang Liu, 2023. "Coupled Propagation Dynamics of Information and Infectious Disease on Two-Layer Complex Networks with Simplices," Mathematics, MDPI, vol. 11(24), pages 1-17, December.

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