During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.

在前所未有的 2019 年冠状病毒病 (COVID-19) 挑战期间，非药物干预措施成为广泛采用的策略，以限制身体运动和互动，以减轻病毒传播。对于态势感知和决策支持，有关人员流动和社交距离的快速且准确的大数据分析对于机构和决策者来说非常宝贵。本文提出了一个大数据驱动的分析框架，每天摄取 TB 级的数据，并定量评估 COVID-19 期间的人员流动趋势。该研究利用美国每月超过 1.5 亿个活跃样本的移动设备位置数据，成功地通过县级的三个主要指标衡量了人员流动性：每人每日平均出行次数；每日平均出行人英里数；以及每天呆在家里的居民百分比。采用一组广义加性混合模型将政策对人口流动的影响与其他混杂效应（包括病毒效应、社会人口效应、天气效应、行业效应和时空自相关）分开。结果表明，该政策对人口流动的影响有限、时间递减且针对特定区域。居家令仅导致人员流动性下降 3.5%-7.9%，而重新开放指南则导致人员流动性增加 1.6%-5.2%。结果还表明，美国各县之间存在合理的空间异质性，其中确诊的 COVID-19 病例数、收入水平、行业结构、年龄和种族分布发挥着重要作用。 该框架生成的数据信息可供公众使用，以便及时了解流动趋势和政策效果，并为进一步遏制病毒传播提供及时的决策支持。