Evidence map›Paper›PMID 33907769›Full record

ArticlemedRxiv : the preprint server for health sciences2021

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 · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 19 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 7 institutions in 3 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.
Shudong HuangCollege of Computer Science, Sichuan University, Chengdu, China.
Piero PolettiCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
Filippo TrentiniCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
Giorgio GuzzettaCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
Valentina MarzianoCenter for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
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.
Ana I BentoDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
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.
Hongjie YuSchool of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
Marco AjelliDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Fondazione Bruno Kessler · ITFudan University · CNSichuan University · CNIndiana University Bloomington · USNortheastern University · USNational Institutes of Health · 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, here we develop a data-driven computational model of SARS-CoV-2 transmission to investigate mechanistically the effect on COVID-19 outbreaks of school closure strategies based on syndromic surveillance and antigen screening of students. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 13.1% (95%CI: 8.6%-20.2 %), due to the low probability of timely symptomatic case identification among the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Should population-level social distancing measures unrelated to schools enable maintaining the reproduction number (

Identifiers

PMID33907769
PMCPMC8077629
OpenAlexW3186011469

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.