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

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.

Disparities in telehealth use: How should the supportive care community respond?

“…In March 2020, the World Health Organization (WHO) declared SARS-CoV-2 (COVID-19) a pandemic [ 1 ]. Many local, state, and national governments instituted guidelines for physical distancing [ 2 , 3 ], and health systems expanded telehealth quickly, and telehealth visits increased across the USA and worldwide, including for supportive and survivorship care in cancer [ 4 , 5 ]. The policy changes in many countries with coverage of the telehealth visit at the same level as an in-person visit facilitated this transformation.…”

Remote Learning of COVID-19 Kinetic Analysis in a Physical Chemistry Laboratory Class

“…The effect of a social event on the R t value is assumed to show up after 1–2 weeks, which is consistent with a recent contact tracing study. 30 For example, several students correlated the drop of the R t value within the first month in Ohio with the stay-at-home order announced on day 18. A further drop in the value is associated with the mask mandate on day 53.…”

Remote Learning of COVID-19 Kinetic Analysis in a Physical Chemistry Laboratory Class

“…Correlation with higher confidence can be found via contact tracing and statistical analysis with a few examples cited. [30][31][32][33] There are inherent dangers with allowing students to be open with guessing the effects of the social mandates, and readers should use caution when allowing students to do so. It is ultimately up to the reader whether this practice is followed, but the reader may also implement guidelines for the students.…”

“…The effect of a social event on the Rt value is assumed to show up after 1-2 weeks, which is consistent with a recent contact tracing study. 30 For example, several students correlated the drop of the Rt value within the first month in Ohio with the stay-at-home order announced on day 18. A further drop in the value is associated with the mask mandate on day 53.…”

“…It is still ongoing research in the literature on the causes of the variation on Rt with a lot of efforts in correlating with governmental policies and more recently also on vaccination. [30][31][32][33] Thus the students were encouraged to associate these peaks and dips with social events, mass gatherings, or regulations such as social distancing depending on the value of Rt. Note that the students should be instructed that these correlations are just hypothetical practices based on the simple social distancing rationale.…”