论文标题
评估美国亚利桑那州亚利桑那州Covid-19动态的人类流动性变化的影响：一项结合Google社区流动性报告的建模研究
Assess the impacts of human mobility change on COVID-19 dynamics in Arizona, U.S.: a modeling study incorporating Google Community Mobility Reports
论文作者
论文摘要
2020年6月，美国亚利桑那州成为世界上最严重的冠状病毒病（Covid-19）景点之一，并于5月中旬升起。但是，随着重新限制的决定，COVID-19案件的数量一直在下降，而亚利桑那州被认为是减慢流行病的良好模型。在本文中，我们旨在研究亚利桑那州的COVID-19-19，并评估人类流动性变化的影响。我们构建了移动性集成的群体易感性驱动的模型，并适合于19个案例的公开数据集和亚利桑那州的移动性变化。我们的模拟表明，通过降低人类的迁移率，峰值时间延迟，并且在所有三个地区的流行病的最终大小都降低了。我们的分析表明，快速有效的决策对于控制人类流动性至关重要，因此是Covid-19的流行病至关重要。在获得疫苗之前，可能需要考虑在亚利桑那州及以后考虑新的Covid-19案件增加的流动性限制。
In June 2020, Arizona, U.S., emerged as one of the world's worst coronavirus disease 2019(COVID-19) spots after the stay-at-home order was lifted in the middle of May. However, with the decisions to reimpose restrictions, the number of COVID-19 cases has been declining, and Arizona is considered to be a good model in slowing the epidemic. In this paper, we aimed to examine the COVID-19 situation in Arizona and assess the impact of human mobility change. We constructed the mobility integrated metapopulation susceptible-infectious-removed model and fitted to publicly available datasets on COVID-19 cases and mobility changes in Arizona. Our simulations showed that by reducing human mobility, the peak time was delayed, and the final size of the epidemic was decreased in all three regions. Our analysis suggests that rapid and effective decision making is crucial to control human mobility and, therefore, COVID-19 epidemics. Until a vaccine is available, reimplementations of mobility restrictions in response to the increase of new COVID-19 cases might need to be considered in Arizona and beyond.
