Early school closures can reduce the first-wave of the COVID-19 pandemic development

Abstract

Aim The COVID-19 pandemic presents serious threats to global public health and the world economy. Therefore, the rapid escalation of the number of cases has led to national government and global interventions. This study aimed to assess the effect of school closures on the COVID-19 pandemic and epidemic trajectories in selected countries. Subject and methods Information on the number of cases and population in each country were taken from official government reports. Dates of educational institutions closure were taken from the UNESCO database. Statistical analyses were performed using Statistica. We summarized the data graphically and descriptively. Results Most of the European countries closed schools in the period of 11–20 of March 2020. However, there was a big difference in the phase of the epidemic on the day of closure. The data indicate that there was a strong correlation between the day of educational facilities closure and the incidence rate in the following days (16th, 30th, and 60th days since the 100th confirmed case in each country). Early closure of schools in analyzed countries is statistically significantly correlated with lower incidence rates further on during the different phases of the epidemic. Thereby closure of schools with delay is statistically significantly correlated with a higher incidence rate in the following days. Conclusion The available data suggest that school closures can potentially reduce transmission during the pandemic, although more research is needed on the effectiveness of these practices.

A Worldwide Journey through Distance Education—From the Post Office to Virtual, Augmented and Mixed Realities, and Education during the COVID-19 Pandemic

“…Across the world, there are currently more than 1.2 billion children in 186 countries who are affected by school closures due to the pandemic [140]. Most European countries decided to close their schools as early as March 2020 and temporarily reintroduce full-time teaching due to distance learning limitations only as a top-down regulation [141,142].…”

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).…”

“…Studies that specifically sought to estimate an effect of school closure policy on SARS-CoV-2 transmission included eight school closure studies(14,23,29,32,37,38,42,44), six school reopening studies(22,23,25,28,44,45), and three school holiday studies. The remaining studies primarily sought to estimate the effect of NPIs (but reported an independent estimate for schools, alongside estimates for other NPIs within their analysis).…”

“…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).…”

“…Some studies dealt with this at the design stage, choosing to study places where school closures were done in (relative) isolation(37) and some at the analytical stage (typically by undertaking regression and having multiple comparator countries). Some studies did not appear to have a mechanism in place to deal with this potential confounding(32,40,44,52). Studies which pooled data from multiple areas also adjusted for other potential confounders, such as population factors (e.g.…”