Epidemic propagation with competitive information in multiplex networks with simplicial complexes

The paper introduces a novel susceptible-vaccinated-infected-recovered (SVIR) epidemic model in a two-layered multiplex network with simplicial complexes, exploring the effect of competitive information on epidemic propagation. Recognizing that information dissemination among individuals is not just pairwise interactions, but also high-order or non-pairwise interactions, the model employs higher-order networks that more accurately reflect real-world phenomena. These networks serve as a representation for information diffusion of online social networks in the upper layer. In this layer, positive and negative preventive information compete for dissemination, both affected by higher-order structures. The lower layer utilizes the SVIR epidemic model to represent the physical contact network responsible for epidemic propagation. Susceptible individuals can choose to get vaccinated during the spread of epidemics. Individual vaccination decisions, based on available information, result in a time-varying vaccination rate in the model that is influenced by competitive information. Positive preventive information encourages vaccination, whereas negative preventive information can act as a deterrent. By employing the microscopic Markov chain approach (MMCA), an explicit expression for the epidemic threshold in multiplex networks with simplicial complexes is derived. The results indicate that the vaccination rate significantly impacts the epidemic threshold. Extensive numerical simulations reveal that simplicial complexes and the vaccination rate affect the epidemic size when it reaches a stable state.