An epidemic model considering multiple factors based on multilayer hypernetworks

Abstract The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases. In order to reduce the risk of infection and prevent the spread of infectious diseases, it is crucial to strengthen individual immunization measures and to restrain the diffusion of negative information relevant to vaccines at the opportune moment. This study develops a three-layer coupling model within the framework of hypernetwork evolution, examining the interplay among negative information, immune behaviour, and epidemic propagation. Firstly, the dynamic topology evolution process of hypernetwork includes node joining, aging out, hyperedge adding and reconnecting. The three-layer communication model accounts for the multifaceted influences exerted by official media channels, subjective psychological acceptance capabilities, self-identification abilities, and physical fitness levels. Each level of the decision-making process is described using the Heaviside step function. Secondly, the dynamics equations of each state and the prevalence threshold are derived using the Microscopic Markov Chain Approach (MMCA). The results show that the epidemic threshold is affected by three transmission processes. Finally, through the simulation testing, it is possible to enhance the intensity of official clarification, improve individual self-identification ability and physical fitness, and thereby promote the overall physical enhancement of society. This, in turn, is beneficial in controlling false information, heightening vaccination coverage, and controlling the epidemic.