One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.

中文翻译：

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移动设备位置数据揭示了美国 COVID-19 大流行期间人员流动对州级居家令的反应


延缓新型冠状病毒 (COVID-19) 传播的一种方法是通过实施旅行限制政策来减少人员旅行。由于缺乏描述大流行期间人员流动的观察到的大规模数据集，了解此类政策的实际人员流动反应仍然是一个挑战。这项研究使用一个集成数据集，其中包括来自美国每月超过 1.5 亿个活跃样本的匿名且受隐私保护的位置数据、COVID-19 病例数据和人口普查人口信息，以揭示 COVID-19 期间和居家隔离期间的流动性变化。美国的国内州令。该研究通过三个重要指标成功量化了人类出行反应：每人每日平均出行次数；每日平均行驶人英里数；以及每天呆在家里的居民百分比。数据分析显示，无论政府采取什么行动，都会出现自发的流动性减少和“地板”现象，即人员流动性达到下限，并在每个州宣布居家令后不久就停止减少。随后开发了一套纵向模型，并证实各州的居家政策仅导致平均每日人口流动性减少约 5%。从数据分析和纵向模型中汲取的经验教训为政府为应对未来另一次 COVID-19 激增或另一次病毒爆发而采取的行动提供了宝贵的见解。