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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: May 4, 2020
Date Accepted: May 20, 2020
Date Submitted to PubMed: May 20, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Delen D, Eryarsoy E, Davazdahemami B

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

JMIR Public Health Surveill 2020;6(2):e19862

DOI: 10.2196/19862

PMID: 32434145

PMCID: 7257477

No Place Like Home: A Cross-National Assessment of the Efficacy of Social Distancing during the COVID-19 Pandemic

  • Dursun Delen; 
  • Enes Eryarsoy; 
  • Behrooz Davazdahemami

ABSTRACT

Background:

In the absence of a cure in the time of pandemics, social distancing measures seem to be the most effective intervention to slow down the spread of disease. Various simulation-based studies have been conducted in the past to investigate the effectiveness of such measures. While those studies unanimously confirm the mitigating effect of social distancing on the 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. A real transactional data, however, can reduce the uncertainty and provide a less noisy picture of social distancing effectiveness.

Objective:

In this paper, we integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics data from ECDC) to study the role of social distancing policies in 26 countries wherein the transmission rate of the COVID-19 pandemic is analyzed over a course of five weeks.

Methods:

Relying on the SIR model and official COVID-19 reports we first calculated weekly transmission rate (β) of the coronavirus disease in 26 countries for five consecutive weeks. Then we integrated that with the Google’s and Apple’s mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between mobility factors and β values.

Results:

Gradient Boosted Trees (GBT) regression analysis showed that changes in mobility patterns, resulted from social distancing policies, explain around 47% of variation in the disease transmission rate.

Conclusions:

Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing down spread of the disease. Apart from providing a less noisy and more generalizable support for the whole social distancing idea, we provide specific insights for public health policy makers as to what locations should be given a higher priority for enforcing social distancing measures. Clinical Trial: N/A


 Citation

Please cite as:

Delen D, Eryarsoy E, Davazdahemami B

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

JMIR Public Health Surveill 2020;6(2):e19862

DOI: 10.2196/19862

PMID: 32434145

PMCID: 7257477

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