Accurately estimating human mobility and gauging its relationship with virus transmission is critical for the control of COVID-19 spreading. Using mobile device location data of over 100 million monthly active samples, we compute origin–destination travel demand and aggregate mobility inflow at each US county from March 1 to June 9, 2020. Then, we quantify the change of mobility inflow across the nation and statistically model the time-varying relationship between inflow and the infections. We find that external travel to other counties decreased by 35% soon after the nation entered the emergency situation, but recovered rapidly during the partial reopening phase. Moreover, our simultaneous equations analysis highlights the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset. This relationship is found to be increasingly stronger in partially reopened regions. Our study provides a quick reference and timely data availability for researchers and decision makers to understand the national mobility trends before and during the pandemic. The modeling results can be used to predict mobility and transmissions risks and integrated with epidemics models to further assess the public health outcomes.

准确估计人类活动能力并确定其与病毒传播的关系对于控制COVID-19传播至关重要。利用每月超过1亿个活动样本的移动设备位置数据，我们计算出2020年3月1日至6月9日美国每个县的始发地-目的地旅行需求和总流动流入。统计流入量和感染之间的时变关系模型。我们发现，在国家进入紧急状态后不久，前往其他县的出境旅行减少了35％，但在部分重新开放阶段迅速恢复。此外，我们的联立方程分析突出显示了COVID-19发作期间流动性流入与感染数量之间呈正相关的动态关系。发现这种关系在部分重新开放的地区越来越强。我们的研究为研究人员和决策者提供了快速参考和及时的数据可用性，以了解大流行之前和期间的国家流动趋势。建模结果可用于预测流动性和传播风险，并可与流行病模型集成以进一步评估公共卫生结果。