Coevolution of information and epidemics in multiplex networks under risk-perception and higher-order interactions

The dynamic interaction between information dissemination and epidemic diffusion has attracted considerable attention. Pairwise interactions alone are insufficient to capture propagation phenomena, and higher-order interactions also play a significant role. Meanwhile, risk-perception emotion critically drives awareness formation and maintains adherence to preventive measures, with heterogeneity in this emotion giving rise to heterogeneous behavioral and cognitive reactions across individuals. To this end, we develop a two-layer coevolution model that encodes higher-order interactions with simplicial complexes and parameterizes risk-perception emotion using historical state and node-level heterogeneity. Additionally, state changes during contagion do not occur instantaneously, emerging only when influence becomes sufficiently strong. To capture the non-linear nature of state transitions, we introduce a threshold-based formulation for risk-perception emotion. Next, we build the probability transition equations using the microscopic Markov chain approach and derive the epidemic threshold. Subsequently, extensive simulations validate the model’s accuracy and theoretical predictions, demonstrating that increasing the pairwise interactions on the information layer, the re-scaled diffusion factor, and the recovery probability, while decreasing the forgetting probability and the damping factor, significantly suppresses the prevalence of the epidemic and increases the outbreak of the epidemic.