The influence of different government policies on the co-evolution of information dissemination, vaccination behavior and disease transmission in multilayer networks

During a disease outbreak, effective intervention policies are crucial for mitigating negative effects and improving public health outcomes. In this paper, a multi-layer coupled network model is proposed to investigate the influence of government policies on the co-evolution of information dissemination, vaccination behavior and disease transmission. The information layer describes the dissemination process of positive and negative information, and considers the influence of government propaganda policies on the information dissemination process. The behavior layer describes the choice of vaccination behavior, and considers the influence of government encouragement policies on individuals' willingness to vaccinate. Meanwhile, the disease layer describes the process of disease transmission, and considers the influence of government intervention policies on the disease transmission control process. By using the Micro Markov Chain Approach (MMCA), we established the state transition equations and derived the diseases prevalence thresholds. The experimental findings demonstrated that in cases where only a single layer of government policy is implemented, intervention policies prove most effective in suppressing disease transmission. When the government policies were combined on two layers, the combination of encouragement and intervention policies emerges as the most effective approach. When government policies work synergistically across three layers, the incidence of infection is minimized, resulting in the most effective disease control. The study provides valuable insights and recommendations for relevant management departments in formulating regulatory strategies.