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Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

Educational Objective
To understand what mobile phone location data can reveal about the efficiency of Travel and Stay-at-Home mandates
1 Credit CME
Key Points

Question  Did human mobility patterns change during stay-at-home orders and were the mobility changes associated with the coronavirus disease 2019 (COVID-19) curve?

Findings  This cross-sectional study using anonymous location data from more than 45 million mobile phones found that median travel distance decreased and stay-at-home time increased across the nation, although there was geographic variation. State-specific empirical doubling time of total COVID-19 cases increased (ie, the spread reduced) significantly after stay-at-home orders were put in place.

Meaning  These findings suggest that stay-at-home social distancing mandates were associated with the reduced spread of COVID-19 when they were followed.


Importance  A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed.

Objective  To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection.

Design, Setting, and Participants  This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases.

Exposure  State-level stay-at-home orders during the COVID-19 pandemic.

Main Outcomes and Measures  The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state.

Results  Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was –0.586 (95% CI, –0.742 to –0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results.

Conclusions and Relevance  These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

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Article Information

Accepted for Publication: July 31, 2020.

Published: September 8, 2020. doi:10.1001/jamanetworkopen.2020.20485

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Gao S et al. JAMA Network Open.

Corresponding Authors: Song Gao, PhD (song.gao@wisc.edu) and Jonathan A. Patz, MD (patz@wisc.edu), University of Wisconsin–Madison, 550 N Park St, Madison, WI 53706.

Author Contributions: Dr Gao had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Gao, Kang, Sethi, Yandell, Patz.

Acquisition, analysis, or interpretation of data: Gao, Rao, Kang, Liang, Kruse, Dopfer, Sethi, Mandujano Reyes.

Drafting of the manuscript: Gao, Rao, Kang, Liang, Kruse, Dopfer, Sethi, Mandujano Reyes, Patz.

Critical revision of the manuscript for important intellectual content: Gao, Rao, Kang, Sethi, Yandell, Patz.

Statistical analysis: Gao, Rao, Kang, Liang, Dopfer, Mandujano Reyes, Yandell.

Obtained funding: Gao.

Administrative, technical, or material support: Gao, Liang, Yandell.

Supervision: Gao.

Conflict of Interest Disclosures: Dr Gao reported receiving grants from National Science Foundation during the conduct of the study. No other disclosures were reported.

Funding/Support: Drs Gao and Patz received funding from grant No. BCS-2027375 from the National Science Foundation.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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