Dynamic analysis of infectious diseases on multi-layer networks based on higher-order interaction

The spread of COVID-19 has an impact on society, so how to mitigate the impact becomes a topic that researchers should explore continually. An effective approach is the coupled awareness-epidemic model on multiplex networks. A double-layer network describes the spread process of information and virus separately. But most studies have not completely considered higher-order structures on complex networks. In this study, we attempt to model and simulate COVID-19 spread on higher-order interactions. Combining the classical unaware-aware-unaware (UAU) process with the susceptible-exposed-asymptomatic-infected-recovered-hospitalized-vaccinated (SEAIRHV) model, the Microscopic Markov equations are established to derive the epidemic threshold. Then numerical simulations are applied to test the analytical results. Finally, the model is validated to fit the actual confirmed data under the given parameters and some suggestions are provided based on this model.