Dataset Information

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

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.

SUBMITTER: Xiong C

PROVIDER: S-EPMC7811592 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Xiong Chenfeng C Hu Songhua S Yang Mofeng M Younes Hannah H Luo Weiyu W Ghader Sepehr S Zhang Lei L

Journal of the Royal Society, Interface 20201216 173

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 ...[more]

PMID: 33323055

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Project description:ImportanceTo cope with the continuing coronavirus disease 2019 (COVID-19) pandemic, state and local officials need information on the effectiveness of policies aimed at curbing disease spread, as well as state-specific characteristics, like the racial mix, associated with increased risks related to the disease.ObjectiveTo investigate whether state-imposed stay-at-home orders (SAHOs) and the proportion of African American population in a state were associated with the state-level COVID-19 cases.Design, setting, and participantsThis cross-sectional study used daily, state-level data on COVID-19 cases, tests, and fatalities from the COVID Tracking Project. Data from March 1 to May 4, 2020, for all states (except Washington state) as well as the District of Columbia were used.ExposuresThe key exposure variables were state-level SAHO (1 if in place, 0 otherwise), and proportion of state population who are African American.Main outcomes and measuresThe primary outcome was daily cumulative COVID-19 case rates. A secondary outcome was subsequent COVID-19 fatality rates, derived using mean cumulative fatality rates 21 to 28 days after each date. Multivariate regression models were estimated.ResultsThe final sample included 3023 pooled state- and day-level observations. The mean (SD) cumulative positive case rate was 103.186 (200.067) cases per 100 000 state population, the mean (SD) cumulative test rate was 744.23 (894.944) tests per 100 000 state population, and the mean (SD) subsequent cumulative fatality rate was 12.923 (21.737) deaths per 100 000 state population. There was a negative association of SAHOs with cumulative case rates (β = -1.166; 95% CI, -1.484 to -0.847; P < .001) and subsequent fatality rates (β = -0.204; 95% CI, -0.294 to -0.113; P < .001). Estimation analyses indicated that expected cumulative case rates would have been more than 200% higher and fatality rates approximately 22% higher if there were no SAHOs, as compared with SAHOs fully in place. A higher proportion of African American population was associated with higher case rates (β = 0.045; 95% CI, 0.014 to 0.077; P = .001) and fatality rates (β = 0.068; 95% CI, 0.044 to 0.091; P < .001).Conclusions and relevanceIn this cross-sectional study, SAHOs were associated with reductions in COVID-19 case rates. These findings could help inform policy makers to address the continued COVID-19 pandemic in the US. The proportion of African American population was positively associated with COVID-19 case rates, and this state-level finding adds to evidence from existing ecological studies using county-level data on racial disparities in COVID-19 infection rates and underlines the urgency of better understanding and addressing these disparities.

Project description:BackgroundAt the beginning of the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) of unprecedented scope and duration were implemented to limit community spread of COVID-19. There remains limited evidence about how these measures impacted the lived experience of affected communities. This study captured the early impacts and coping strategies implemented in King County, Washington, one of the first U.S. communities impacted by COVID-19.MethodsWe conducted a cross-sectional web-based survey of 793 English- and Spanish-speaking adult King County residents from March 18, 2020 -May 30, 2020, using voluntary response sampling. The survey included close- and open-ended questions on participant demographics, wellbeing, protective actions, and COVID-19-related concerns, including a freeform narrative response to describe the pandemic's individual-, family- and community-level impacts and associated coping strategies. Descriptive statistics were used to analyze close-ended questions, and qualitative content analysis methods were used to analyze free-form narrative responses.ResultsThe median age of participants was 45 years old, and 74% were female, 82% were White, and 6% were Hispanic/Latinx; 474 (60%) provided a qualitative narrative. Quantitative findings demonstrated that higher percentages of participants engaged in most types of COVID-19 protective behaviors after the stay-at-home order was implemented and schools and community spaces were closed, relative to before, and that participants tended to report greater concern about the pandemic's physical health or healthcare access impacts than the financial or social impacts. Qualitative data analysis described employment or financial impacts (56%) and vitality coping strategies (65%), intended to support health or positive functioning.ConclusionsThis study documented early impacts of the COVID-19 pandemic and the NPIs implemented in response, as well as strategies employed to cope with those impacts, which can inform early-stage policy formation and intervention strategies to mitigate the negative impacts. Future research should explore the endurance and evolution of the early impacts and coping strategies throughout the multiyear pandemic.

Dataset Information

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

Publications

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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