Abstract

Drawing on risk methods from volcano crises, we developed a rapid COVID-19 infection model for the partial return of pupils to primary schools in England in June and July 2020, and a full return in September 2020. The model handles uncertainties in key parameters, using a stochastic re-sampling technique, allowing us to evaluate infection levels as a function of COVID-19 prevalence and projected pupil and staff headcounts. Assuming average national adult prevalence, for the first scenario (as at 1 June 2020) we found that between 178 and 924 [90% CI] schools would have at least one infected individual, out of 16 769 primary schools in total. For the second return (July), our estimate ranged between 336 (2%) and 1873 (11%) infected schools. For a full return in September 2020, our projected range was 661 (4%) to 3310 (20%) infected schools, assuming the same prevalence as for 5 June. If national prevalence fell to one-quarter of that, the projected September range would decrease to between 381 (2%) and 900 (5%) schools but would increase to between 2131 (13%) and 9743 (58%) schools if prevalence increased to 4× June level. When regional variations in prevalence and school size distribution were included in the model, a slight decrease in the projected number of infected schools was indicated, but uncertainty on estimates increased markedly. The latter model variant indicated that 82% of infected schools would be in areas where prevalence exceeded the national average and the probability of multiple infected persons in a school would be higher in such areas. Post hoc, our model projections for 1 September 2020 were seen to have been realistic and reasonable (in terms of related uncertainties) when data on schools' infections were released by official agencies following the start of the 2020/2021 academic year.

Highlights

  • On 11 May 2020, the UK government announced that selected primary school-age children in England would return to school on 1 June 2020

  • While official guidelines for re-opening schools were issued by the Department of Education (DfE) and formed the policy basis for risk mitigation in schools, some individual schools adopted their own bespoke strategies [1]

  • We transferred numerical hazard and risk methods—developed for protecting people threatened by volcanic eruptions [3]—to enumerate potential infection levels in primary schools, taking formal account of estimates of uncertainties associated with attendance and prevalence factors

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Summary

Introduction

On 11 May 2020, the UK government announced that selected primary school-age children in England would return to school on 1 June 2020. This study formed part of a quantitative hazard assessment of the threat of encountering COVID-19 infections in primary school pupils and staff. We transferred numerical hazard and risk methods—developed for protecting people threatened by volcanic eruptions [3]—to enumerate potential infection levels in primary schools, taking formal account of estimates of uncertainties associated with attendance and prevalence factors. The study used an efficient stochastic uncertainty modelling tool to construct an estimator for infection hazard levels in primary schools in England, with the intention this would be a first step in developing a risk model including infection transmission. We were able to follow up on our initial projections concerning the numbers of pupils' returning to primary schools in England in June–July 2020 and the potential numbers of infection incidents, and compare these with surveillance data reported by various government agencies. Our approach can be configured for different kinds of educational operation, such as universities and further education colleges, and it is currently being developed to inform the sampling strategy and programme for a project of infection testing in selected schools [5]

Hazard and risk terminology
Methodology
Data sources for model inputs
Results
Base rate infection hazard levels
School size effect
Spatial variations in prevalence
Effects of school size and multiple infections
Comparison with observations
Reviewing scenario projections
Incidence-to-prevalence conversion factor—sensitivity analysis
Further work
Informing policy decisions
Full Text
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