How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

Abstract

The role of complete lockdowns in reducing the reproduction ratios (Rt) of COVID-19 is now established. However, the persisting reality in many countries is no longer a complete lockdown, but restrictions of varying degrees using different choices of Non-pharmaceutical interaction (NPI) policies. A scientific basis for understanding the effectiveness of these graded NPI policies in reducing the Rt is urgently needed to address the concerns on personal liberties and economic activities. In this work, we develop a systematic relation between the degrees of NPIs implemented by the 26 cantons in Switzerland during March 9 to September 13 and their respective contributions to the Rt. Using a machine learning framework, we find that Rt which should ideally be lower than 1.0, has significant contributions in the post-lockdown scenario from the different activities - restaurants (0.0523 (CI. 0.0517-0.0528)), bars (0.030 (CI. 0.029-0.030)), and nightclubs (0.154 (CI. 0.154-0.156)). Activities which keep the land-borders open (0.177 (CI. 0.175-0.178)), and tourism related activities contributed comparably 0.177 (CI. 0.175-0.178). However, international flights with a quarantine did not add further to the Rt of the cantons. The requirement of masks in public transport and secondary schools contributed to an overall 0.025 (CI. 0.018-0.030) reduction in Rt, compared to the baseline usage even when there are no mandates. Although causal relations are not guaranteed by the model framework, it nevertheless provides a fine-grained justification for the relative merits of choice and the degree of the NPIs and a data-driven strategy for mitigating Rt.

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).…”

“…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. 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).…”

“…Among school closure studies, 18 studies(14,15,19,20,24,26,29,31–34,37,39,42–44,50,51) reported effects on incidence, 11(14,19,21,30,38–40,42,46,52,53) on mortality, one(37) on hospital admissioins and mortality, and eight(18,21,23,35,36,43,47,48) on an estimate of the effective Reproductive number (R) (derived from incidence and/or mortality data). Of the school reopening studies, six reported effects on incidence(16,22,24,28,44,45), two on hospitalisations(25, 44), and four on R(23,27,35,43). Two school holiday studies reported an effect on incidence(16, 41), while the other reported on mortality(17).…”