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Effects of behavioral observability and social proof on the coupled epidemic-awareness dynamics in multiplex networks

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  • Huayan Pei
  • Huanmin Wang
  • Guanghui Yan

Abstract

Despite much progress in exploring the coupled epidemic-awareness dynamics in multiplex networks, little attention has been paid to the joint impacts of behavioral observability and social proof on epidemic spreading. Since both the protective actions taken by direct neighbors and the observability of these actions have essential influence on individuals’ decisions. Thus, we propose a UAPU-SIR model by integrating the effects of these two factors into the decision-making process of taking preventive measures. Specifically, a new state called taken protective actions is introduced into the original unaware-aware-unaware (UAU) model to characterize the action-taken state of individuals after getting epidemic-related information. Using the Microscopic Markov Chain Approach (MMCA), the methods and model are described, and the epidemic threshold is analytically derived. We find that both observability of protecting behaviors and social proof can reduce the epidemic prevalence and raise the epidemic threshold. Moreover, only if observability of protection actions reaches a certain threshold can accelerating information diffusion is able to inhibit disease spreading and result in higher epidemic threshold. We also discover that, reducing the forgetting rate of information is able to decrease epidemic size.

Suggested Citation

  • Huayan Pei & Huanmin Wang & Guanghui Yan, 2024. "Effects of behavioral observability and social proof on the coupled epidemic-awareness dynamics in multiplex networks," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-16, July.
  • Handle: RePEc:plo:pone00:0307553
    DOI: 10.1371/journal.pone.0307553
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    References listed on IDEAS

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    1. Kabir, K.M. Ariful & Kuga, Kazuki & Tanimoto, Jun, 2019. "Analysis of SIR epidemic model with information spreading of awareness," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 118-125.
    2. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
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