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<p>As a prerequisite for prevention strategies to successfully control the spread of a pandemic crisis, it is necessary to assume that individuals are capable and willing to cooperate. From the perspective of strategic decision-making, cooperation is an unattainable approach to the Prisoner’s Dilemma game. In this game, people’s actions are shaped by two key factors: the desire to exploit others for personal gain and the fear of being betrayed. This dynamic frequently drives individuals to act in self-interest, fueled by distrust or fear, over cooperative behavior, resulting in suboptimal outcomes. Such behavior prevents achieving outcomes that would benefit everyone more if mutual cooperation is pursued. The current study combines an <i>SIRS</i> epidemic model with the Replicator Equation (RE) of evolutionary games to investigate the correlation between the transmission of infection and the tendency of humans to cooperate with each other while they are under the influence of the epidemic. The model that has been constructed emerges multiple stable states, some of which are totally or partly cooperative. The existence of such states makes it possible to bring the illness under control. The phenomenon of stability alteration experienced by transcritical bifurcation is investigated, and numerical simulations are employed to demonstrate various theoretical conclusions. The process of determining how changes in various parameters of a model affect the propagation of an ailment is referred to as sensitivity analysis. In this particular instance, Latin hypercube sampling is applied in order to carry out uncertainty and sensitivity analysis on key input parameters. The correlation coefficients of Kendall’s tau and Spearman’s rank are computed in order to develop a more in-depth understanding of the ways in which these uncertainties influence the dynamics of the illness. Furthermore, incorporating seasonal variations in the infection rate introduces diverse dynamic behaviors within the system. The results of this research shed light on the tremendous influence that a variety of elements, including governmental measures, social behavioral dynamics, and public responses, as well as the frequency and amplitude of transmission, have on the evolving patterns of epidemic dynamics.</p>

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The role of cooperation in epidemic control: insights from an SIRS model with evolutionary games

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Protyusha Dutta,
  • Guruprasad Samanta,
  • Juan J. Nieto

摘要

As a prerequisite for prevention strategies to successfully control the spread of a pandemic crisis, it is necessary to assume that individuals are capable and willing to cooperate. From the perspective of strategic decision-making, cooperation is an unattainable approach to the Prisoner’s Dilemma game. In this game, people’s actions are shaped by two key factors: the desire to exploit others for personal gain and the fear of being betrayed. This dynamic frequently drives individuals to act in self-interest, fueled by distrust or fear, over cooperative behavior, resulting in suboptimal outcomes. Such behavior prevents achieving outcomes that would benefit everyone more if mutual cooperation is pursued. The current study combines an SIRS epidemic model with the Replicator Equation (RE) of evolutionary games to investigate the correlation between the transmission of infection and the tendency of humans to cooperate with each other while they are under the influence of the epidemic. The model that has been constructed emerges multiple stable states, some of which are totally or partly cooperative. The existence of such states makes it possible to bring the illness under control. The phenomenon of stability alteration experienced by transcritical bifurcation is investigated, and numerical simulations are employed to demonstrate various theoretical conclusions. The process of determining how changes in various parameters of a model affect the propagation of an ailment is referred to as sensitivity analysis. In this particular instance, Latin hypercube sampling is applied in order to carry out uncertainty and sensitivity analysis on key input parameters. The correlation coefficients of Kendall’s tau and Spearman’s rank are computed in order to develop a more in-depth understanding of the ways in which these uncertainties influence the dynamics of the illness. Furthermore, incorporating seasonal variations in the infection rate introduces diverse dynamic behaviors within the system. The results of this research shed light on the tremendous influence that a variety of elements, including governmental measures, social behavioral dynamics, and public responses, as well as the frequency and amplitude of transmission, have on the evolving patterns of epidemic dynamics.