The Unconfirmed Information-Disease Coupling Transmission Considering Individual Information Literacy and Policy Intervention

The outbreak of new diseases generates a significant amount of unconfirmed information, and policy interventions can effectively influence both information and disease spread. This paper proposes a two-stage information-disease coupling model to examine the policy interventions on disease dissemination. The model quantifies policy containment efficacy across pre- and post-confirmation phases. The information layer describes the propagation of unconfirmed and confirmed information, considering the role of information literacy in the spread of unconfirmed information. The disease layer models the disease transmission process, while also incorporating the impact on government intervention policies on both the information and disease propagation. Using the Micro Markov Chain Approach (MMCA), we establish the state transition tree and equations, followed by simulation experiments. The results indicate that intervening before the disease reaches a steady state is the most effective strategy. When policy interventions simultaneously target both layers, interventions addressing both confirmed information and disease can generate a synergistic effect, resulting in more effective disease control. This study provides valuable recommendations for policymakers.