Coupled dynamics of competitive information and epidemic propagation in multiplex networks via evolutionary game theory

This paper introduces a two-layer multiplex network model to explore the interplay between information diffusion, behavioral adaptation, and epidemic propagation. The upper layer captures the competitive spreading, forgetting, and switching of positive and negative information, while the lower layer represents epidemic dynamics with infection, vaccination, and recovery processes. The two layers are interconnected through infection-induced dissemination of positive information and awareness-driven behavioral modification. Evolutionary game theory with Fermi updating is employed to describe adaptive vaccination strategies, where payoff functions integrate awareness, infection risk, and protection costs. Analytical results derived using the microscopic Markov chain approach (MMCA) show that the epidemic threshold is determined by the largest eigenvalue of a weighted structural matrix incorporating topology, information diffusion, and behavioral feedback. Numerical simulations confirm the theoretical predictions, indicating that positive information and adaptive vaccination increase the epidemic threshold, whereas misinformation and high vaccination costs reduce it. These findings highlight the crucial influence of information credibility, adaptive behavior, and network structure on epidemic outcomes, providing insights for designing integrated public health intervention strategies.