Effects of isolation and information dissemination on epidemic dynamics in multiplex networks

Epidemics pose major challenges to public health, and effective isolation strategies are essential for curbing disease transmission. However, traditional epidemic models often overlook two critical aspects: the duration of isolation for infected individuals and the role of information dissemination. To address these limitations, we propose a novel epidemic model based on a two-layer multiplex network that captures the interactions between isolation strategies, disease transmission, and the spread of disease-related information. In this framework, one layer represents information dissemination, while the other represents disease dynamics incorporating isolation measures. Using the microscopic Markov chain (MMC) approach, we derive expressions for the epidemic threshold and analyze its relationship with the basic reproduction number (R0), showing that isolation significantly influences R0. Results from Monte Carlo (MC) simulations closely match those from the MMC analysis, validating the model’s accuracy. Our findings demonstrate that isolation strategies not only suppress disease transmission but also enhance information dissemination through positive feedback. Notably, under low infection rates, the duration of isolation is more effective than its frequency in controlling outbreaks. These insights provide practical guidance for designing optimized isolation strategies that balance epidemiological effectiveness with social and economic considerations, offering valuable input for evidence-based public health policy.