Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

• Published in: Journal of the Royal Society interface 2020-12, Vol.17 (173), p.20200344-20200344
• Main Authors: Xiong, Chenfeng, Hu, Songhua, Yang, Mofeng, Younes, Hannah, Luo, Weiyu, Ghader, Sepehr, Zhang, Lei
• Format: Article
• Language: English
• Subjects: Computers, Handheld; COVID-19 - epidemiology; COVID-19 - prevention & control; Data Interpretation, Statistical; Geographic Information Systems; Humans; Life Sciences–Mathematics interface; Longitudinal Studies; Models, Statistical; Pandemics - prevention & control; Physical Distancing; SARS-CoV-2; Travel - legislation & jurisprudence; Travel - statistics & numerical data; Travel - trends; United States - epidemiology
• ISSN: 1742-5689; 1742-5662
• DOI: 10.1098/rsif.2020.0344

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.