Deep-reinforcement-learning-based optimization for intra-urban epidemic control considering spatiotemporal orderliness

Abstract

When planning intra-urban control measures for epidemics with significant societal impact, it is essential to consider the spatiotemporal orderliness of interventions, thus mitigating the disruption to daily life. For instance, improving intervention consistency among highly interacted sub-regions and avoid frequent and significant changes of interventions over time can be effective. However, existing studies on optimizing epidemic control overlooked the need for spatiotemporal consistency and stability of the interventions, potentially impacting their practicality and public compliance. To fill this gap, this study systematically conceptualized and quantified spatiotemporal orderliness for intra-urban epidemic control. A deep-reinforcement-learning (DRL) framework integrating the spatiotemporal orderliness was proposed to optimize the interventions across sub-regions over time. Taking Shenzhen, China as a study area, we solve a joint control plan for 74 sub-regions based on a meta-population SEIR epidemic model with a real-world intra-urban mobility network. The results demonstrate that the proposed model can effectively generate tailored dynamic interventions for sub-regions, significantly improving spatiotemporal orderliness. Furthermore, the effectiveness and generalizability of proposed model are demonstrated under different urban structures and transmissibility of respiratory viruses. Overall, this study provides a DRL-based tool for planning intra-urban epidemic control measures with enhanced spatiotemporal orderliness, potentially aiding future epidemic preparedness.