Impact of higher-order interactions and individual emotional heterogeneity on information-disease coupled dynamics in multiplex networks.

The dynamic interplay between information diffusion and disease spreading has received much attention in recent years. Studies have shown that pairwise interactions are not sufficient to portray the social contagion process, and that higher-order group interactions comprise an important factor influencing the social contagion. Therefore, the introduction of higher-order interactions into information-disease modeling should be considered. Studies have also shown that emotions play an increasingly important role in epidemic prevention and control, and that there is strong individual heterogeneity in the cognitive and behavioral responses. To this end, we propose a new coupled information-disease spreading model, which uses simplicial complexes to describe higher-order interactions, quantifies individual emotions in terms of both network topology and node attributes, and introduces three emotional heterogeneity indicators. We derive the dynamic equations of the model using the microscopic Markov chain method and analyze the effects of higher-order interactions and individual emotional heterogeneity on the dynamic evolution process through numerical simulations. The results show that the introduction of simplicial complexes in the modeling process can capture the bistable state in the system, and that more simplicial complexes and greater heterogeneity in the effect of individual emotions on immunity accelerate the spreading speed of disease and increase the outbreak size, while greater heterogeneity in the individual emotional threshold and the response of individual emotions to information has the opposite effect. Finally, based on the conclusions obtained, we provide references and suggestions for the government and other related departments to formulate disease prevention and control strategies. • A two-layer network model coupling information diffusion with disease spreading through individual emotions is proposed. • Higher-order interactions are introduced simultaneously in the information and disease layers through simplicial complex. • Three indicators of individual emotional heterogeneity are proposed. • A computational approach to quantify individual emotion is developed. [ABSTRACT FROM AUTHOR]