Changes in Reproductive Rate of SARS-CoV-2 Due to Non-pharmaceutical Interventions in 1,417 U.S. Counties

Abstract

In response to the rapid spread of the novel coronavirus, SARS-CoV-2, the U.S. has largely delegated implementation of non-pharmaceutical interventions (NPIs) to local governments on the state and county level. This staggered implementation combined with the heterogeneity of the U.S. complicates quantification the effect of NPIs on the reproductive rate of SARS-CoV-2. We describe a data-driven approach to quantify the effect of NPIs that relies on county-level similarities to specialize a Bayesian mechanistic model based on observed fatalities. Using this approach, we estimate change in reproductive rate, R_t, due to implementation of NPIs in 1,417 U.S. counties. We estimate that as of May 28th, 2020 1,177 out of the considered 1,417 U.S. counties have reduced the reproductive rate of SARS-CoV-2 to below 1.0. The estimated effect of any individual NPI, however, is different across counties. Stay-at-home orders were estimated as the only effective NPI in metropolitan and urban counties, while advisory NPIs were estimated to be effective in more rural counties. The expected level of infection predicted by the model ranges from 0 to 28.7% and is far from herd immunity even in counties with advanced spread. Our results suggest that local conditions are pertinent to containment and re-opening decisions.

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

“…the unit of analysis was national or regional. 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).…”

“…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).…”

“…There was significant heterogeneity in the study findings (table 3): 17 studies(14,24,31,32,34–38,40,42–44,48–51) reported that closing schools was associated with a reduction in transmission rates; nine (15,18,20,23,26,29,30,39,47) found no association between school closures and transmission; five (19,21,33,46,53) reported mixed findings with evidence of a reduction in transmission in some analyses but not others; and one study(52) reported that school closures were associated with an increase in mortality. The reported effect size of closing schools ranged from precise estimates of no effect(26) to approximately halving the incidence(14); and from approximately doubling mortality(52), to approximately halving mortality(14).…”

“…Of these studies, 11(14,24,32,34–36,40,42,48–50) reported that school closures were associated with significantly reduced community transmission of SARS-CoV-2, seven(15,18,20,26,30,39,47) reported no association, and four(19,21,33,46) reported mixed findings. Those studies found to be at higher risk of bias, generally because they were judged not to have adjusted appropriately for NPIs, testing, or sociodemographic data, tended to report reductions in transmission; whereas those studies at lower risk of bias were as likely to report null effects as they were reductions (see figure 2).…”