The worldwide outbreak of COVID-19 has posed a dire threat to the public. Human mobility has changed in various ways over the course of the pandemic. Despite current studies on common mobility metrics, research specifically on state-to-state mobility is very limited. By leveraging the mobile phone location data from over 100 million anonymous devices, we estimate the population flow between all states in the United States. We first analyze the temporal pattern and spatial differences of between-state flow from January 1, 2020 to May 15, 2020. Then, with repeated measures ANOVA and post-hoc analysis, we discern different time-course patterns of between-state population flow by pandemic severity groups. A further analysis shows moderate to high correlation between the flow reduction and the pandemic severity, the strength of which varies with different policies. This paper is promising in predicting imported cases.

中文翻译：

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美国 COVID-19 期间的州际旅行分析

COVID-19 的全球爆发对公众构成了可怕的威胁。在大流行期间，人类的流动性以各种方式发生了变化。尽管目前对通用流动性指标进行了研究，但专门针对州与州之间流动性的研究非常有限。通过利用来自超过 1 亿个匿名设备的手机位置数据，我们估计了美国所有州之间的人口流动。我们首先分析了2020年1月1日至2020年5月15日州际人口流动的时间格局和空间差异。然后，通过重复测量方差分析和事后分析，我们辨别了州际人口流动的不同时间过程模式由大流行严重性组。进一步的分析表明，流量减少与大流行严重程度之间存在中度至高度相关性，其强弱因政策不同而异。这篇论文在预测输入病例方面很有前景。