Evidence map›Paper›PMID 33413741›Full record

ArticleEuro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin2021

Impact of mass testing during an epidemic rebound of SARS-CoV-2: a modelling study using the example of France.

Paolo Bosetti, Cécile Tran Kiem, Yazdan Yazdanpanah, Arnaud Fontanet, Bruno Lina, Vittoria Colizza, Simon Cauchemez

Abstract read
In one paragraph

Article in Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  20. Accuracy of saliva and nasopharyngeal sampling for detection of SARS-CoV-2 in community screening: a multicentric cohort study.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2021
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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

7 authors.

Paolo BosettiMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Cécile Tran KiemMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Yazdan YazdanpanahInfections Antimicrobials Modelling Evolution (IAME) UMR 1137, University of Paris, Paris, France.
Arnaud FontanetEmerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France.
Bruno LinaNational Reference Center for Respiratory Viruses, Department of Virology, Infective Agents Institute, North Hospital Network, Lyon, France.
Vittoria ColizzaINSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France.
Simon CauchemezMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We used a mathematical model to evaluate the impact of mass testing in the control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Under optimistic assumptions, one round of mass testing may reduce daily infections by up to 20-30%. Consequently, very frequent testing would be required to control a quickly growing epidemic if other control measures were to be relaxed. Mass testing is most relevant when epidemic growth remains limited through a combination of interventions.

Indexed as

Mass ScreeningModels, TheoreticalCOVID-19COVID-19 TestingEpidemicsFranceHumansCovid-19epidemicmass testingSARS-CoV-2

Identifiers

PMID33413741
PMCPMC7791601

What OpenQuestion holds

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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.