Evidence map›Paper›PMID 39753869›Full record

ArticleCommunications medicine2025

A modeling study to define guidelines for antigen screening in schools and workplaces to mitigate COVID-19 outbreaks.

Yong Dam Jeong, Keisuke Ejima, Kwang Su Kim, Shoya Iwanami, William S Hart, Robin N Thompson, Il Hyo Jung, Shingo Iwami, Marco Ajelli, Kazuyuki Aihara

Abstract read
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. 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
    Article
  3. Article
  4. 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.

Yong Dam Jeong *Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan.ORCID http://orcid.org/0000-0002-3970-7690
Keisuke Ejima *Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore. keisuke.ejima@ntu.edu.sg.ORCID http://orcid.org/0000-0002-1185-3987
Kwang Su Kim *Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
Shoya IwanamiInterdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan.ORCID http://orcid.org/0000-0003-1474-5348
William S HartMathematical Institute, University of Oxford, Oxford, UK.
Robin N ThompsonMathematics Institute, University of Warwick, Coventry, UK.ORCID http://orcid.org/0000-0001-8545-5212
Il Hyo JungDepartment of Mathematics, Pusan National University, Busan, South Korea.
Shingo IwamiInterdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan. iwami.iblab@bio.nagoya-u.ac.jp.
Marco AjelliLaboratory for Computational Epidemiology and Public Health Department of Epidemiology and Biostatistics, Indiana University School of Public Health-, Bloomington, IN, USA.
Kazuyuki AiharaInternational Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan.

Funding

MIDAS Coordination CenterU24GM132013 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOCHHEISER, HARRY S · 2019 to 2023
$8.1M
NIGMS NIH HHS U24 GM132013
6 · The paper itself

Abstract

backgroundIn-person interaction offers invaluable benefits to people. To guarantee safe in-person activities during a COVID-19 outbreak, effective identification of infectious individuals is essential. In this study, we aim to analyze the impact of screening with antigen tests in schools and workplaces on identifying COVID-19 infections.

methodsWe assess the effectiveness of various screening test strategies with antigen tests in schools and workplaces through quantitative simulations. The primary outcome of our analyses is the proportion of infected individuals identified. The transmission process at the population level is modeled using a deterministic compartmental model. Infected individuals are identified through screening tests or symptom development. The time-varying sensitivity of antigen tests and infectiousness is determined by a viral dynamics model. Screening test strategies are characterized by the screening schedule, sensitivity of antigen tests, screening duration, timing of screening initiation, and available tests per person.

resultsHere, we show that early and frequent screening is the key to maximizing the effectiveness of the screening program. For example, 44.5% (95% CI: 40.8-47.5) of infected individuals are identified by daily testing, whereas it is only 33.7% (95% CI: 30.5-37.3) when testing is performed at the end of the program duration. If high sensitivity antigen tests (Detection limit:

conclusionsHigh sensitivity antigen tests, high frequency screening tests, and immediate initiation of screening tests are important to safely restart educational and economic activities in-person. Our computational framework is useful for assessing screening programs by incorporating situation-specific factors.

Identifiers

PMID39753869
PMCPMC11699287

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LicenceCC BY-NC-ND
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Registered trials

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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.