Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA.
J R Soc Interface
; 17(173): 20200344, 2020 12.
Article
in English
| MEDLINE | ID: covidwho-978651
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Travel
/
Computers, Handheld
/
Pandemics
/
SARS-CoV-2
/
COVID-19
Type of study:
Cohort study
/
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
J R Soc Interface
Year:
2020
Document Type:
Article
Affiliation country:
Rsif.2020.0344
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