Coupled dynamics of information and epidemics in time-varying multiplex networks

Abstract Revealing the relationship between epidemic transmission mechanisms and information flow is crucial for optimizing public health response strategies. Given the dynamic and higher-order characteristics of networks, which capture complex behaviors more effectively, this paper proposes a dual-layer time-varying network model based on an activity-driven algorithm, in which second-order dependencies are assigned to each node. The upper layer represents the information dissemination layer, including simplex interactions and mass media. The lower layer constitutes the epidemic transmission layer, illustrating the actual interactions among individuals. By formulating the microscopic Markov (MMC) chain equations, the outbreak threshold and the effective reproduction parameter of the model are calculated in this study. Subsequently, Monte Carlo (MC) simulations are used to further validate the propagation characteristics. The results indicate that collective effects can effectively suppress epidemic transmission; however, as the 2-simplex transmission rate increases, the suppressive effect weakens. Individual characteristics also exert a significant influence on the spread of both information and epidemics.