Sustainable COVID-19 Policy Responses With Urban Mobility Network Epidemic Models

Abstract

The COVID-19 pandemic has challenged countries worldwide to strike a balance between implementing epidemic control measures and maintaining economic activity. In response, many countries have adopted sustainable, precise, region-specific, and multilevel prevention and control measures. To apply these measures more effectively and purposefully, it is imperative to quantify their impact on the transmission of COVID-19 within urban areas. Here, we propose a dynamic metapopulation susceptible-exposed-infectious-removed (SEIR) model that incorporates the urban mobility network to simulate the spread of COVID-19 in Beijing and investigate the effects of precise intervention measures. Our proposed model accurately fits the real epidemic trajectory, even with the significant changes in human mobility patterns before and after the epidemic. Additionally, it can also serve as a useful policy evaluation tool by simulating the impact of perturbations in mobility networks on epidemic transmission dynamics. Based on this tool, our results demonstrate that point-of-interest capacity limitation measures can significantly reduce the number of infections with only a minor loss of urban mobility. Furthermore, we show that community dynamic management measures can effectively control and mitigate COVID-19 spread while enabling the normal operation of most economic and social activities. By quantifying the impact of precise intervention measures on new infections and mobility losses, our model enables a cost-benefit analysis of these measures, thus informing targeted and sustainable policy responses to COVID-19.