Evidence map›Paper›PMID 36074818›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools.

Akira Endo 遠藤彰, CMMID COVID-19 Working Group, Mitsuo Uchida 内田満夫, Yang Liu 刘扬, Katherine E Atkins, Adam J Kucharski, Sebastian Funk

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Minimising school disruption under high incidence conditions due to the Omicron variant in France, Switzerland, Italy, in January 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023
    Article
  7. Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools.Proceedings of the National Academy of Sciences of the United States of America · 2022
    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

7 authors.

Akira Endo 遠藤彰Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom.ORCID 0000-0001-6377-7296
CMMID COVID-19 Working GroupThe Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom.
Mitsuo Uchida 内田満夫Graduate School of Medicine, Gunma University, Gunma 371-8511, Japan.ORCID 0000-0002-9928-8897
Yang Liu 刘扬Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom.
Katherine E AtkinsDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom.ORCID 0000-0001-5250-0558
Adam J KucharskiDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom.
Sebastian FunkDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom.ORCID 0000-0002-2842-3406

Funding

Medical Research Council MC_PC_19065Wellcome Trust 206250Wellcome Trust 206250/Z/17/ZWellcome Trust 208812/Z/17/ZWellcome Trust 210758
6 · The paper itself

Abstract

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society's responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak.

Indexed as

Disease OutbreaksPandemicsRespiratory Tract InfectionsSchoolsChildComputer SimulationCOVID-19HumansInfluenza, Humanclass sizeinfluenzamathematical modelschoolsocial network

Identifiers

PMID36074818
PMCPMC9478679

What OpenQuestion holds

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LicenceCC BY
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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.