Coupled dynamics of information diffusion and disease transmission considering vaccination and time-varying forgetting probability

Vaccination is critical for controlling infectious diseases, but negative vaccination information can lead to vaccine hesitancy. To study how the interplay between information diffusion and disease transmission impacts vaccination and epidemic spread, we propose a novel two-layer multiplex network model that integrates an unaware–acceptant–negative–unaware (UANU) information diffusion model with a susceptible–vaccinated–exposed-infected-susceptible (SVEIS) epidemiological framework. This model includes individual exposure and vaccination statuses, time-varying forgetting probabilities, and information conversion thresholds. Through the microscopic Markov chain approach (MMCA), we derive dynamic transition equations and the epidemic threshold expression, validated by Monte Carlo simulations. Using MMCA equations, we predict vaccination densities and analyze parameter effects on vaccination, disease transmission, and the epidemic threshold. Our findings suggest that promoting positive information, curbing the spread of negative information, enhancing vaccine effectiveness, and promptly identifying asymptomatic carriers can significantly increase vaccination rates, reduce epidemic spread, and raise the epidemic threshold.