Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA |
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Authors: | Chenfeng Xiong Songhua Hu Mofeng Yang Hannah Younes Weiyu Luo Sepehr Ghader Lei Zhang |
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Affiliation: | 1.Maryland Transportation Institute (MTI), University of Maryland, College Park, MD 20742, USA;2.Center for Shock, Trauma, and Anesthesiology Research (STAR), University of Maryland, Baltimore, MD 21201, USA |
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Abstract: | 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. |
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Keywords: | human mobility COVID-19 mobile device location data behavioural response |
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