Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020

PLoS One. 2021 Jun 30;16(6):e0253071. doi: 10.1371/journal.pone.0253071. eCollection 2021.

Abstract

Background: Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease.

Methods: We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks.

Results: Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases.

Discussion: This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies' relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / epidemiology*
  • COVID-19 / prevention & control
  • COVID-19 / transmission*
  • Europe / epidemiology
  • Health Policy
  • Humans
  • Linear Models
  • Pandemics
  • Physical Distancing*
  • Quarantine / statistics & numerical data

Grants and funding

CC, LG, MT acknowledge partial support from the Lagrange Project of the Institute for Scientific Interchange (ISI) Foundation funded by CRT Foundation, and from the European Union’s Horizon H2020 research and innovation programme under grant agreements 101003688 (EpiPose) and 101016233 (PERISCOPE). TCT was supported by the William F. Milton Fund of the Harvard University Office of the Vice Provost for Research. In 2020 GW served as a visiting scientist at Google LLC (Mountain View, CA). LRW’s work on this project was unfunded. In addition, Google LLC provided support in the form of salaries for authors (JH, VE, SV, AF, KG, AB, AP, CK, AS, CS, SB, MA, MH, AO, KC, GC, TS, GAW, EG), but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Several of these authors were also involved in the development and launch of Google’s Community Mobility Reports. All manuscripts authored by employees of Google are reviewed prior to journal submission to ensure that it meets Google’s standards. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.