Quantifying the efficiency of non-pharmaceutical interventions against SARS-COV-2 transmission in Europe

Abstract

Since the emergence of SARS-CoV-2, governments around the World have implemented a combination of public health responses based on non-pharmaceutical interventions (NPIs), with significant social and economic consequences. Though most European countries have overcome the first epidemic wave, it remains of high priority to quantify the efficiency of different NPIs to inform preparedness for an impending second wave. In this study, combining capture-recapture methods with Bayesian inference in an age-structured mathematical model, we use a unique European dataset compiled by the European Centre for Disease Control (ECDC) to quantify the efficiency of 24 NPIs and their combinations (referred to as public health responses, PHR) in reducing SARS-Cov-2 transmission rates in 32 European countries. Of 166 unique PHR tested, we found that median decrease in viral transmission was 74%, which is enough to suppress the epidemic. PHR efficiency was positively associated with the number of NPIs implemented. We found that bans on mass gatherings had the largest effect among NPIs, followed by school closures, teleworking, and stay home orders. Partial implementation of most NPIs resulted in lower than average response efficiency. This first large-scale estimation of NPI and PHR efficiency against SARS-COV-2 transmission in Europe suggests that a combination of NPIs targeting different population groups should be favored to control future epidemic waves.

Do school closures and school reopenings affect community transmission of COVID-19? A systematic review of observational studies

“…Included studies are described in Table 1, grouped by exposure type and study design. Of these, 32 studies(14,15,18–21,23,24,26,29–40,42–44,46–53) reported the effect of school closures on community transmission of SARS-CoV-2, 11(16,22–25,27,28,35,43–45) examined school reopening, and 3(16,17,41) investigated the effect of school holidays. Some studies considered more than one exposure.…”

“…Of the school closure studies, 13 reported data from a single country or region (the USA (n=10)(14,19–21,33,37,42,47–49), Italy (n=1)(23), Japan (n=1)(29), and Switzerland (n=1)(43)); four reported discrete estimates for several countries(26,38,44,53); and 15 studies pooled data from multiple countries (globally (n=8)(31,34– 36,39,46,50,51), Europe only (n=2)(24, 30), Europe and other high income countries (n=5)(15,18,32,40,52)). The studies on school reopening generally reported on single countries (Germany (n=2)(22, 28), USA (n=1)(25), Switzerland (n=1)(43), Belgium (n=1)(27), Israel (n=1)(45), Italy (n=1)(23)), but one reported discrete estimates for three countries (Denamrk, Germany and Norway)(44), two pooled data from multiple countries globally(16, 35), and one pooled data from multiple European countries(24). Of the three school holiday studies, one reported on Germany(41), one pooled data from 24 countries globally(16), and one pooled data from multiple European countries(17).…”

“…The majority of studies (n=24) did not specify the type of school setting being studied. However, eight studies specified that they were reporting on primary and secondary schools only(14,16,18,19,27,29,37,49), and six additionally include early years settings(22–24,44,45,48). The two remaining studies used the date of primary school (n=1)(15) or secondary school (n=1)(43) closure as their exposure date, but did not indicate this was temporally distinct from closure of the other setting.…”

“…The two remaining studies used the date of primary school (n=1)(15) or secondary school (n=1)(43) closure as their exposure date, but did not indicate this was temporally distinct from closure of the other setting. Very few studies reported independent effect sizes for different setting types: two closure studies(24, 48) and four reopening studies(16,22,24,44).…”

“…The studies utilised different analytic approaches: regression models (n=24)(14,17,19– 21,25,26,28,30,31,33,35,36,39–42,44,46,48,49,51–53), Bayesian modelling (n=3)(15,18,47), comparison to a synthetic control group (n=4)(24,34,38,44), machine learning approaches (n=2)(43, 50), time series analysis (n=1)(29), and visual representation of changes in transmission over time compared against the timing of school policy interventions, with or without formal statistical analysis (n=4)(16,22,37,45). We identified three study designs used to estimate the effect of school closures: pooled multiple-area before-after comparisons (n=22)(14,15,18–21,24,26,30,32–36,39,40,42,46–50), within-area before-after comparisons(n=7)(23,29,37,38,43,44,53), and pooled multiple-area comparisons of interventions in place at a fixed time point (n=3)(31,51,52).…”