Evidence map›Paper›PMID 36181503›Full record

ArticleJournal of global health2022

Transmission of SARS-CoV-2 in a primary school setting with and without public health measures using real-world contact data: A modelling study.

Lixiang Yan, Stella Talic, Holly Wild, Danijela Gasevic, Dragan Gasević, Dragan Ilic, Joanne Deppeler, Deborah Corrigan, Roberto Martinez-Maldonado, James Trauer

Open access · goldAbstract read
In one paragraph

Article in Journal of global health, 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
1.4field-weighted citation impact, top 18% of its field
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, 9 citations in OpenAlex.

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  7. A dynamic causal modeling of the second outbreak of COVID-19 in Italy.Advances in statistical analysis : AStA : a journal of the German Statistical Society · 2023
    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

10 authors at 3 institutions in 2 countries.

Lixiang YanFaculty of Information Technology, Monash University, Clayton, Victoria, Australia.
Stella TalicSchool of Public Health and Preventive Medicine, Monash University Clayton, Victoria, Australia.
Holly WildPublic Health & Health Sciences, Torrens University Australia, Melbourne, Victoria, Australia.
Danijela GasevicSchool of Public Health and Preventive Medicine, Monash University Clayton, Victoria, Australia.
Dragan GasevićFaculty of Information Technology, Monash University, Clayton, Victoria, Australia.
Dragan IlicSchool of Public Health and Preventive Medicine, Monash University Clayton, Victoria, Australia.
Joanne DeppelerFaculty of Education, Monash University, Clayton, Victoria, Australia.
Deborah CorriganFaculty of Education, Monash University, Clayton, Victoria, Australia.
Roberto Martinez-MaldonadoFaculty of Information Technology, Monash University, Clayton, Victoria, Australia.
James TrauerSchool of Public Health and Preventive Medicine, Monash University Clayton, Victoria, Australia.
Monash University · AUTorrens University Australia · AUUniversity of Edinburgh · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Stringent public health measures have been shown to influence the transmission of SARS-CoV-2 within school environments. We investigated the potential transmission of SARS-CoV-2 in a primary school setting with and without public health measures, using fine-grained physical positioning traces captured before the COVID-19 pandemic. Methods: Approximately 172.63 million position data from 98 students and six teachers from an open-plan primary school were used to predict a potential transmission of SARS-CoV-2 in primary school settings. We first estimated the daily average number of contacts of students and teachers with an infected individual during the incubation period. We then used the Reed-Frost model to estimate the probability of transmission per contact for the SARS-CoV-2 Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron variant (B.1.1.529). Finally, we built a binomial distribution model to estimate the probability of onward transmission in schools with and without public health measures, including face masks and physical distancing. Results: An infectious student would have 49.1 (95% confidence interval (CI) = 46.1-52.1) contacts with their peers and 2.00 (95% CI = 1.82-2.18) contacts with teachers per day. An infectious teacher would have 47.6 (95% CI = 45.1-50.0) contacts with students and 1.70 (95% CI = 1.48-1.92) contacts with their colleague teachers per day. While the probability of onward SARS-CoV-2 transmission was relatively low for the Alpha and Delta variants, the risk increased for the Omicron variant, especially in the absence of public health measures. Onward teacher-to-student transmission (88.9%, 95% CI = 88.6%-89.1%) and teacher-to-teacher SARS-CoV-2 transmission (98.4%, 95% CI = 98.5%-98.6%) were significantly higher for the Omicron variant without public health measures in place. Conclusions: Our findings illustrate that, despite a lower frequency of close contacts, teacher-to-teacher close contacts demonstrated a higher risk of transmission per contact of SARS-CoV-2 compared to student-to-student close contacts. This was especially significant with the Omicron variant, with onward transmission more likely occurring from teacher index cases than student index cases. Public health measures (eg, face masks and physical distance) seem essential in reducing the risk of onward transmission within school environments.

Indexed as

COVID-19SARS-CoV-2HumansPandemicsPublic HealthSchools

Identifiers

PMID36181503
PMCPMC9526455
OpenAlexW4298326440

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.