Evidence map›Paper›PMID 39836691›Full record

ArticlePLoS medicine2025

Estimating the impact of school closures on the COVID-19 dynamics in 74 countries: A modelling analysis.

Romain Ragonnet, Angus E Hughes, David S Shipman, Michael T Meehan, Alec S Henderson, Guillaume Briffoteaux, Nouredine Melab, Daniel Tuyttens, Emma S McBryde, James M Trauer

Abstract read
In one paragraph

Article in PLoS medicine, 2025. 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
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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

10 authors.

Romain RagonnetSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-8520-2362
Angus E HughesSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.ORCID https://orcid.org/0000-0002-1543-1757
David S ShipmanSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.ORCID https://orcid.org/0009-0004-4663-1366
Michael T MeehanAustralian Institute of Tropical Health and Medicine, James Cook University, Douglas, Australia.
Alec S HendersonAustralian Institute of Tropical Health and Medicine, James Cook University, Douglas, Australia.ORCID https://orcid.org/0000-0001-9233-5059
Guillaume BriffoteauxMathematics and Operational Research Department, University of Mons, Mons, Belgium.ORCID https://orcid.org/0000-0002-4878-2000
Nouredine MelabCNRS CRIStAL, Inria Lille, Université de Lille, Lille, France.
Daniel TuyttensMathematics and Operational Research Department, University of Mons, Mons, Belgium.
Emma S McBrydeUniversity of Queensland Centre for Clinical Research (UQCCR), University of Queensland, Brisbane, Australia.ORCID https://orcid.org/0000-0002-9570-9172
James M TrauerSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.ORCID https://orcid.org/0000-0002-0991-1631

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSchool closures have been a prominent component of the global Coronavirus Disease 2019 (COVID-19) response. However, their effect on viral transmission, COVID-19 mortality and health care system pressure remains incompletely understood, as traditional observational studies fall short in assessing such population-level impacts. METHODS AND

findingsWe used a mathematical model to simulate the COVID-19 epidemics of 74 countries, incorporating observed data from 2020 to 2022 and historical school closure timelines. We then simulated a counterfactual scenario, assuming that schools remained open throughout the study period. We compared the simulated epidemics in terms of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections, deaths, and hospital occupancy pressure. We estimated that school closures achieved moderate to significant burden reductions in most settings over the period 2020 to 2022. They reduced peak hospital occupancy pressure in nearly all countries, with 72 out of 74 countries (97%) showing a positive median estimated effect, and median estimated effect ranging from reducing peak hospital occupancy pressure by 89% in Brazil to increasing it by 19% in Indonesia. The median estimated effect of school closures on COVID-19 deaths ranged from a 73% reduction in Thailand to a 7% increase in the United Kingdom. We estimated that school closures may have increased overall COVID-19 mortality (based on median estimates) in 9 countries (12%), including several European nations and Indonesia. This is attributed to changes in population-level immunity dynamics, leading to a concentration of the epidemic during the Delta variant period, alongside an upward shift in the age distribution of infections. While our estimates were associated with significant uncertainty, our sensitivity analyses exploring the impact of social mixing assumptions revealed robustness in our country-specific conclusions. The main study limitations include the fact that analyses were conducted at the national level, whereas school closure policies often varied by region. Furthermore, some regions, including Africa, were underrepresented due to insufficient data informing the model.

conclusionsOur analysis revealed nuanced effects of school closures on COVID-19 dynamics, with reductions in COVID-19 impacts in most countries but negative epidemiological effects in a few others. We identified critical mechanisms for consideration in future policy decisions, highlighting the unpredictable nature of emerging variants and potential shifts in infection demographics associated with school closures.

Indexed as

COVID-19SchoolsGlobal HealthHumansModels, TheoreticalSARS-CoV-2

Identifiers

PMID39836691
PMCPMC11793732

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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.