Epidemic dynamics in physical-information-social multilayer networks

During epidemic outbreaks, information dissemination helps to improve individual infection prevention, while social institutions influence the transmission through measures like government interventions, media campaigns, and hospital resource allocation. Here we develop a tripartite physical-information-social epidemic model and derive the corresponding kinetic equations in different scales by using the Microscopic Markov Chain Approach and mean-field approximations. The proposed model enables adaptive social attention allocation, achieving a lower epidemic size at steady state with limited containment resources compared to traditional static interventions. The basic reproduction number and epidemic thresholds are explicitly derived by the next generation matrix method. Our results reveal that (1) active information exchange curbs disease transmission, (2) stronger governmental influence on media and hospitals decreases disease transmission, particularly in hospital nodes, and (3) time-delayed feedback alters the peak of epidemic size while leaving the steady state unchanged. In fixed community structures, groups with frequent physical contact but weak information access (e.g., students) exhibit higher infection rates. For diverse communities, weaker physical layer heterogeneity but stronger information layer heterogeneity inhibits epidemic outbreaks. These findings offer valuable insights for epidemic prevention and control strategies.