No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic.

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  • Additional Information
    • Source:
      Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101669345 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-2960 (Electronic) Linking ISSN: 23692960 NLM ISO Abbreviation: JMIR Public Health Surveill Subsets: MEDLINE
    • Publication Information:
      Original Publication: Toronto : JMIR Publications, [2015]-
    • Subject Terms:
      Communicable Disease Control* ; Public Policy*; Coronavirus Infections/*epidemiology ; Coronavirus Infections/*prevention & control ; Pandemics/*prevention & control ; Pneumonia, Viral/*epidemiology ; Pneumonia, Viral/*prevention & control; COVID-19 ; Data Analysis ; Europe/epidemiology ; Humans
    • Abstract:
      Background: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing.
      Objective: The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks.
      Methods: Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and β values.
      Results: Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates.
      Conclusions: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.
      (©Dursun Delen, Enes Eryarsoy, Behrooz Davazdahemami. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 28.05.2020.)
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    • Contributed Indexing:
      Keywords: COVID-19; machine learning; pandemic; public health; social distancing
    • Publication Date:
      Date Created: 20200521 Date Completed: 20200603 Latest Revision: 20201218
    • Publication Date:
      20221213
    • Accession Number:
      PMC7257477
    • Accession Number:
      10.2196/19862
    • Accession Number:
      32434145