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Prediction of COVID-19 Social Distancing Adherence (SoDA) on the United States county-level

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  • Myles Ingram

    (Columbia University Irving Medical Center)

  • Ashley Zahabian

    (Columbia University)

  • Chin Hur

    (Columbia University Irving Medical Center)

Abstract

Social distancing policies are currently the best method of mitigating the spread of the COVID-19 pandemic. However, adherence to these policies vary greatly on a county-by-county level. We used social distancing adherence (SoDA) estimated from mobile phone data and population-based demographics/statistics of 3054 counties in the United States to determine which demographics features correlate to adherence on a countywide level. SoDA scores per day were extracted from mobile phone data and aggregated from March 16, 2020 to April 14, 2020. 45 predictor features were evaluated using univariable regression to determine their level of correlation with SoDA. These 45 features were then used to form a SoDA prediction model. Persons who work from home prior to the COVID-19 pandemic (β = 0.259, p

Suggested Citation

  • Myles Ingram & Ashley Zahabian & Chin Hur, 2021. "Prediction of COVID-19 Social Distancing Adherence (SoDA) on the United States county-level," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-7, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00767-0
    DOI: 10.1057/s41599-021-00767-0
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    References listed on IDEAS

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    1. Aroon Chande & Seolha Lee & Mallory Harris & Quan Nguyen & Stephen J. Beckett & Troy Hilley & Clio Andris & Joshua S. Weitz, 2020. "Real-time, interactive website for US-county-level COVID-19 event risk assessment," Nature Human Behaviour, Nature, vol. 4(12), pages 1313-1319, December.
    2. Allcott, Hunt & Boxell, Levi & Conway, Jacob & Gentzkow, Matthew & Thaler, Michael & Yang, David, 2020. "Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    3. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
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    Cited by:

    1. Semen Budennyy & Alexey Kazakov & Elizaveta Kovtun & Leonid Zhukov, 2022. "New drugs and stock market: how to predict pharma market reaction to clinical trial announcements," Papers 2208.07248, arXiv.org, revised Aug 2022.
    2. Anne Marie Novak & Adi Katz & Michal Bitan & Shahar Lev-Ari, 2022. "The Association between the Sense of Coherence and the Self-Reported Adherence to Guidelines during the First Months of the COVID-19 Pandemic in Israel," IJERPH, MDPI, vol. 19(13), pages 1-13, June.

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