The Impact of Interventions on Information Diffusion and Epidemic Spreading on Multilayer Networks

The pandemic severely impact the normal functioning of human society. During outbreaks, epidemic information spreads quickly online, while governments enact strategies to halt its spread. This paper establishes a dual-layer coupled network model to explore the co-evolutionary dissemination of epidemic-related information and epidemic under interventions, termed the UAU-SEIQR model. The upper layer network describes the diffusion of epidemic information, where official media dynamically adjust media promotion intensity to facilitate the dissemination of epidemic-related information. The lower layer network represents the process of epidemic spread, incorporating real-time adjustments of quarantine intervention strategies affecting the epidemic spreading process. Microscopic Markov Chain Approach (MMCA) is employed to establish state transition equations describing the model. Numerical simulation experiments validate the dynamic equations, further analyzing the effects of various parameters on epidemic spreading and threshold. The results show that strengthening the dissemination of epidemic-related information and implementing intervention measures can significantly suppress epidemic spreading.