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

被引:132
作者
Gao, Song [1 ]
Rao, Jinmeng [1 ]
Kang, Yuhao [1 ]
Liang, Yunlei [1 ]
Kruse, Jake [1 ]
Dopfer, Dorte [2 ]
Sethi, Ajay K. [3 ]
Mandujano Reyes, Juan Francisco [2 ]
Yandell, Brian S. [4 ]
Patz, Jonathan A. [3 ]
机构
[1] Univ Wisconsin Madison, Dept Geog, GeoDS Lab, Madison, WI USA
[2] Univ Wisconsin Madison, Sch Vet Med, Madison, WI USA
[3] Univ Wisconsin Madison, Sch Med & Publ Hlth, Madison, WI USA
[4] Univ Wisconsin Madison, Stat & Amer Family Insurance Data Sci Inst, Madison, WI USA
基金
美国国家科学基金会;
关键词
D O I
10.1001/jamanetworkopen.2020.20485
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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. This cross-sectional study uses anonymous mobile phone location data to examine travel and home dwell time patterns before and after enactment of stay-at-home orders in US states during the coronavirus disease 2019 (COVID-19) pandemic. 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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页数:13
相关论文
共 49 条
  • [1] [Anonymous], 2018, NAT SCI DATA
  • [2] Aggregated mobility data could help fight COVID-19
    Buckee, Caroline O.
    Balsari, Satchit
    Chan, Jennifer
    Crosas, Merce
    Dominici, Francesca
    Gasser, Urs
    Grad, Yonatan H.
    Grenfell, Bryan
    Halloran, M. Elizabeth
    Kraemer, Moritz U. G.
    Lipsitch, Marc
    Metcalf, C. Jessica E.
    Meyers, Lauren Ancel
    Perkins, T. Alex
    Santillana, Mauricio
    Scarpino, Samuel V.
    Viboud, Cecile
    Wesolowski, Amy
    Schroeder, Andrew
    [J]. SCIENCE, 2020, 368 (6487) : 145 - 146
  • [3] Centers for Disease Control and Prevention, COVID 19 CAS US
  • [4] Centers for Disease Control and Prevention, SIM DIFF FLU COVID 1
  • [5] Centers for Disease Control and Prevention, 2022, Social distancing, quarantine, and isolation
  • [6] Chinazzi M, 2020, SCIENCE, V368, P395, DOI [10.1126/science.aba9757, 10.1101/2020.02.09.20021261]
  • [7] Unique in the Crowd: The privacy bounds of human mobility
    de Montjoye, Yves-Alexandre
    Hidalgo, Cesar A.
    Verleysen, Michel
    Blondel, Vincent D.
    [J]. SCIENTIFIC REPORTS, 2013, 3
  • [8] Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
    Ferretti, Luca
    Wymant, Chris
    Kendall, Michelle
    Zhao, Lele
    Nurtay, Anel
    Abeler-Dorner, Lucie
    Parker, Michael
    Bonsall, David
    Fraser, Christophe
    [J]. SCIENCE, 2020, 368 (6491) : 619 - +
  • [9] Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age
    Gao, Song
    [J]. SPATIAL COGNITION AND COMPUTATION, 2015, 15 (02) : 86 - 114
  • [10] Gao Song, 2020, SIGSPATIAL SPECIAL, V12, P16