Dynamics of Information–Epidemic Coupled Spreading in Age-Heterogeneous Multilayer Networks
TL;DR
Simulation results show that age-dependent information diffusion, infection-induced feedback, the initial location of information seeds, and network topology all affect the epidemic spreading process, and theoretical insights into age-stratified information intervention strategies in heterogeneous populations are provided.
Abstract
To investigate the interaction between protective information diffusion and disease transmission, this study proposes an information–epidemic coupled spreading model on an age-heterogeneous multilayer network. The population is divided into three age groups: youth, middle-aged individuals, and older adults. Disease transmission and protective information diffusion are represented in the physical disease layer and the virtual information layer, respectively. A UA-SIR framework is adopted to describe the coupled evolution of nodes’ information awareness states and epidemic states. The model further incorporates age-specific spreading parameters, the effects of awareness on infection risk and recovery processes, and the feedback effect of infection status on information diffusion. Node-level state transition equations are established, and the epidemic threshold is analyzed using an age-aggregated next-generation matrix. Simulation results show that age-dependent information diffusion, infection-induced feedback, the initial location of information seeds, and network topology all affect the epidemic spreading process. Empirical analyses based on four real infectious disease datasets further indicate that introducing information diffusion can reduce the infection peak, flatten the epidemic curve, and delay the peak time in some scenarios. These findings provide theoretical insights into age-stratified information intervention strategies in heterogeneous populations.