Evidence map›Paper›PMID 34540240›Full record

ArticleRoyal Society open science2021

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

W P Aspinall, R S J Sparks, M J Woodhouse, R M Cooke, J H Scarrow, CoMMinS Project

Abstract read
In one paragraph

Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

W P AspinallSchool of Earth Sciences, University of Bristol, Bristol BS8 1RJ, UK.ORCID 0000-0001-6014-6042
R S J SparksSchool of Earth Sciences, University of Bristol, Bristol BS8 1RJ, UK.ORCID 0000-0001-7173-2899
M J WoodhouseSchool of Earth Sciences, University of Bristol, Bristol BS8 1RJ, UK.ORCID https://orcid.org/0000-0002-2198-6791
R M CookeInstitute of Applied Mathematics, Delft University of Technology, Building 28, Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands.ORCID https://orcid.org/0000-0003-0643-1971
J H ScarrowDepartamento de Mineralogía y Petrología, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain.ORCID https://orcid.org/0000-0001-8585-8679
CoMMinS Project

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Bayesian belief networkEngland primary school COVID-19 risksscenario sensitivity testsschools openingstochastic uncertainty analysis

Identifiers

PMID34540240
PMCPMC8441136

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.