Coupled information–disease spreading dynamics on multilayer weighted networks considering higher-order interactions

The dissemination of epidemic-related information and the evolution of infections are closely interdependent in many real human interaction contexts. To describe this interaction, an information–epidemic coevolution framework is constructed over a bilayer higher-order weighted network. The information layer describes the circulation of epidemic-related information, while the contact layer characterizes the infection process through physical contacts. Weighted links are introduced into both layers to characterize the heterogeneity of social relationships and contact strengths. In the information diffusion layer, 2-simplices are introduced to characterize group-based collective interactions and the reinforcement effect of information spreading. A bidirectional coupling mechanism is also considered, where information acquisition reduces the actual infection probability of aware individuals, while disease infection promotes the acquisition of disease-related information. Based on the microscopic Markov chain method, the dynamical equations are derived, and the critical condition for epidemic outbreak is obtained by linearizing the system near the disease-free equilibrium. Numerical comparisons with quasi-stationary Monte Carlo (QS-MC) simulations show that the theoretical predictions agree well with the stochastic simulation results, particularly in the active spreading regime. The findings reveal that stronger pairwise or higher-order information diffusion increases the aware density and suppresses the infected density. The topology of the infection-contact layer plays an important role in determining the critical threshold and stationary densities. In addition, increasing the information-layer weight mean or the recovery rate raises the outbreak threshold, whereas increasing the disease-layer weight mean promotes disease spreading. These findings provide useful insights into coupled spreading dynamics on weighted higher-order networks.