4.6 Article

Pupils returning to primary schools in England during 2020: rapid estimations of punctual COVID-19 infection rates

期刊

ROYAL SOCIETY OPEN SCIENCE
卷 8, 期 9, 页码 -

出版社

ROYAL SOC
DOI: 10.1098/rsos.202218

关键词

England primary school COVID-19 risks; schools opening; stochastic uncertainty analysis; Bayesian belief network; scenario sensitivity tests

资金

  1. Royal Society education policy unit
  2. Royal Society RAMP initiative for COVID-19
  3. Medical Research Council [MR/V0285545/1]
  4. UK MRC Integrative Epidemiology Unit at the University of Bristol [MC_UU_00011/5]

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Drawing on risk methods from volcano crises, a rapid COVID-19 infection model was developed for the partial return and full return of pupils to primary schools in England. The model handles uncertainties in key parameters, evaluating infection levels based on COVID-19 prevalence and projected pupil numbers. The study found that the projected number of infected schools varied with changes in prevalence rates.
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 4x 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.

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