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<p>In clustered networks, the influence of higher-order interactions on disease dynamics has received limited attention. This paper investigates the susceptible-infected-susceptible epidemic model on clustered regular networks using the pair mean-field approach, where higher-order effects are explicitly considered in dynamic analysis. An auxiliary system is constructed to derive the analytical expression of the epidemic threshold. Through theoretical analysis and numerical simulations, we systematically explore the impacts of network clustering and higher-order interactions on epidemic invasion and persistence thresholds. The results reveal that stronger higher-order interactions reduce both invasion and persistence thresholds, while an increase in the network clustering coefficient raises the epidemic threshold. Furthermore, these phenomena cannot be fully captured by the conventional one-vertex mean-field approximation. This work highlights the vital roles of clustering structures and higher-order interactions in epidemic spreading and verifies the effectiveness of the pair mean-field method for analyzing higher-order network dynamics.</p>

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Dynamics of pairwise-based SIS epidemic models on clustered networks with higher-order interactions

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Qingchu Wu,
  • Zhaoyan Wu

摘要

In clustered networks, the influence of higher-order interactions on disease dynamics has received limited attention. This paper investigates the susceptible-infected-susceptible epidemic model on clustered regular networks using the pair mean-field approach, where higher-order effects are explicitly considered in dynamic analysis. An auxiliary system is constructed to derive the analytical expression of the epidemic threshold. Through theoretical analysis and numerical simulations, we systematically explore the impacts of network clustering and higher-order interactions on epidemic invasion and persistence thresholds. The results reveal that stronger higher-order interactions reduce both invasion and persistence thresholds, while an increase in the network clustering coefficient raises the epidemic threshold. Furthermore, these phenomena cannot be fully captured by the conventional one-vertex mean-field approximation. This work highlights the vital roles of clustering structures and higher-order interactions in epidemic spreading and verifies the effectiveness of the pair mean-field method for analyzing higher-order network dynamics.