Evidence map›Paper›PMID 35031600›Full record

ArticleNature communications2022

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

Quan-Hui Liu, Juanjuan Zhang, Cheng Peng, Maria Litvinova, Shudong Huang, Piero Poletti, Filippo Trentini, Giorgio Guzzetta, Valentina Marziano, Tao Zhou and 7 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
5.7field-weighted citation impact, top 3% 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

27 citing papers in PubMed, 47 citations in OpenAlex.

  1. Article
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  4. VIBES: A multiscale modeling approach integrating within-host and between-hosts dynamics in epidemics.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  18. 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
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 8 institutions in 4 countries.

Quan-Hui Liu *College of Computer Science, Sichuan University, Chengdu, China.
Juanjuan Zhang *School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
Cheng PengSchool of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
Maria LitvinovaDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.ORCID 0000-0001-6393-1943
Shudong HuangCollege of Computer Science, Sichuan University, Chengdu, China.
Piero PolettiCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.ORCID 0000-0001-5453-5199
Filippo TrentiniDondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy.ORCID 0000-0001-6915-7282
Giorgio GuzzettaCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.ORCID 0000-0002-9296-9470
Valentina MarzianoCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.ORCID 0000-0003-2842-7906
Tao ZhouBig Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Cecile ViboudDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0003-3243-4711
Ana I BentoDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.ORCID 0000-0001-7728-007X
Jiancheng LvCollege of Computer Science, Sichuan University, Chengdu, China.
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
Stefano MerlerCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.ORCID 0000-0002-5117-0611
Hongjie YuSchool of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China. yhj@fudan.edu.cn.ORCID 0000-0002-6335-5648
Marco AjelliLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA. marco.ajelli@gmail.com.ORCID 0000-0003-1753-4749
Fondazione Bruno Kessler · ITFudan University · CNIndiana University Bloomington · USSichuan University · CNBocconi University · ITNational Institutes of Health · USNortheastern University · USUniversity of Electronic Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0-26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

Indexed as

Models, StatisticalComputer SimulationCOVID-19COVID-19 Serological TestingHumansItalyMass ScreeningPhysical DistancingQuarantineSARS-CoV-2SchoolsStudents

Identifiers

PMID35031600
PMCPMC8760266
OpenAlexW4226003826

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.