The frequent emergence of new infectious diseases poses a serious threat to human health and social development. In the current digital age, the widespread application of online social networks has accelerated the dissemination of information regarding these diseases, influencing the interplay between disease spread and information diffusion. To address this, we propose the $UAU$ – $SEPIR$ model, a novel two-layered network propagation model inspired by the early stages of the COVID-19 outbreak. This model, constructed using a micro-Markov-chain approach, features an upper layer for information diffusion (the $UAU$ model) and a lower layer for disease propagation (the $SEPIR$ model). We derive the epidemic threshold, which is influenced by the dynamics of information diffusion, the network topology of disease spread, and the pathways from exposed to infected to recovered individuals. Through extensive random simulations, we confirmed the validity of our model. The research findings reveal that both model parameters and network topology play crucial roles in shaping the interaction between information diffusion and disease spread. Additionally, in random networks, adaptive behavior of individuals significantly enhances the inhibition of disease transmission. Overall, Our study provides theoretical insights into the interplay between social-network dynamics and the early outbreak stages, offering valuable support for disease prevention and control strategies.
