Research on epidemic prevention and control in megacities based on the spatiotemporal mobility of populations with different economic attributes

In response to the significant challenges posed by major public health emergencies to public health and socio-economic development in megacities, this study explores pandemic control strategies that balance epidemic containment and economic development. First, a model based on individual, group, and spatial movement networks is developed to examine the impact of spatiotemporal movement of populations with different economic attributes on epidemic evolution and economic development in megacities. This model integrates the classical SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased) model to construct an epidemic evolution model for megacities. Second, addressing the conflict between epidemic spread and economic loss, a multi-objective optimization model is proposed to minimize both epidemic spread and economic losses. The model optimizes regional lockdowns and population flow restrictions through a combined decision-making approach. An improved NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm is designed to solve this model based on its specific characteristics. Finally, the model’s effectiveness is validated using the case of epidemic control in the main urban area of Chongqing during the COVID-19 pandemic. Sensitivity analysis of key parameters provides valuable management insights for epidemic prevention and control in megacities.