Yamamoto Nao, Jiang Bohan, Wang Haiyan
School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, 85287, USA.
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA.
Infect Dis Model. 2021;6:503-513. doi: 10.1016/j.idm.2021.02.004. Epub 2021 Mar 4.
The outbreak of COVID-19 disrupts the life of many people in the world. In response to this global pandemic, various institutions across the globe had soon issued their prevention guidelines. Governments in the US had also implemented social distancing policies. However, those policies, which were designed to slow the spread of COVID-19, and its compliance, have varied across the states, which led to spatial and temporal heterogeneity in COVID-19 spread. This paper aims to propose a spatio-temporal model for quantifying compliance with the US COVID-19 mitigation policies at a regional level. To achieve this goal, a specific partial differential equation (PDE) is developed and validated with short-term predictions. The proposed model describes the combined effects of transboundary spread among state clusters in the US and human mobilities on the transmission of COVID-19. The model can help inform policymakers as they decide how to react to future outbreaks.
新冠疫情扰乱了世界上许多人的生活。为应对这一全球大流行,全球各机构很快发布了预防指南。美国政府也实施了社交距离政策。然而,这些旨在减缓新冠病毒传播的政策及其执行情况在各州各不相同,这导致了新冠病毒传播的时空异质性。本文旨在提出一种时空模型,用于在区域层面量化美国新冠疫情缓解政策的执行情况。为实现这一目标,开发了一个特定的偏微分方程(PDE)并通过短期预测进行了验证。所提出的模型描述了美国各州集群之间的跨境传播和人员流动对新冠病毒传播的综合影响。该模型可以帮助政策制定者决定如何应对未来的疫情爆发。