Optimizing Sustainable Mobility Interventions for Efficient Epidemic Containment

Learning from the lessons of the COVID-19 pandemic, nations are increasingly recognizing the imperative to develop sustainable mobility interventions that effectively balance epidemic control and economic stability. In response, we study a novel network immunity problem: the formulation of precise capacity limitation measures for each point of interest (POI) node within the urban mobility network. The aim is to maximize epidemic containment under the fixed resource budget for mobility intervention. To achieve this, we establish a metapopulation model on urban inter-POI networks. Our proposed model accurately fits real epidemic trajectories, demonstrating resilience to significant shifts in human movement patterns pre- and post-epidemic. Leveraging this model, we derive the generalized basic reproduction number and reframe the original problem as one that minimizes $R_{0}$ under budgetary constraints. We devise a greedy capacity reduction algorithm to approximately solve these problems. Subsequently, we conduct extensive experiments on large-scale urban networks that connect 4,335 residential communities to 14,936 POIs with 5.7 million daily edges. Compared to baseline methods, our algorithm consistently achieves higher efficiency and accuracy in reducing $R_{0}$ and maximizing epidemic containment. Notably, it can effectively minimize the risk of epidemic spread within the city without imposing significant constraints on overall urban mobility.