Evidence map›Paper›PMID 41926357›Full record

ArticlePLoS computational biology2026

Identifying the optimal rapid antigen test for screening and determining the end of isolation: A modeling study.

Yong Dam Jeong, William S Hart, Masahiro Ishikane, Kwang Su Kim, Jong Hyuk Byun, Il Hyo Jung, Montie T Harrison, Kazuyuki Aihara, Norio Ohmagari, Christopher B Brooke and 3 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Yong Dam JeongInterdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
William S HartMathematical Institute, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-2504-6860
Masahiro IshikaneDisease Control and Prevention Centre, National Centre for Global Health and Medicine, Japan Institute for Health Security, Tokyo, Japan.ORCID 0000-0002-4719-651X
Kwang Su KimDepartment of Science System Simulation, Pukyong National University, Busan, South Korea.
Jong Hyuk ByunDepartment of Mathematics and Institute of Mathematical Sciences, Pusan National University, Busan, South Korea.
Il Hyo JungDepartment of Mathematics and Institute of Mathematical Sciences, Pusan National University, Busan, South Korea.
Montie T HarrisonInterdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
Kazuyuki AiharaInternational Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan.
Norio OhmagariDisease Control and Prevention Centre, National Centre for Global Health and Medicine, Japan Institute for Health Security, Tokyo, Japan.
Christopher B BrookeDepartment of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
Ruian KeTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Robin N ThompsonMathematical Institute, University of Oxford, Oxford, United Kingdom.
Shingo IwamiInterdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.ORCID 0000-0002-1780-350X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the COVID-19 pandemic, rapid antigen tests (RATs) were used to detect infections, improving the effectiveness of targeted non-pharmaceutical interventions (NPIs). However, RATs based on either nasal swab or saliva samples were used, raising the question as to which type of RAT is most effective at detecting viral infections. Here, we develop a model-driven computational framework to assess different RATs and identify the most suitable test for a specified purpose, such as infection screening or determining the end of isolation, for various viral infections. Using symptomatic COVID-19 cases as a case study, we found that saliva-based RATs reduced transmission risk on average by 6.2% (95% CI: 6.1 - 6.3) compared to nasal-based RATs in the pre-symptomatic period. In addition, by ending isolation of infected individuals who have developed symptoms when consecutive RATs return negative results, the mean risk of transmission was reduced by 5.9% (95% CI: 5.7 - 6.1) using a saliva-based RAT compared to using a nasal-based RAT. These findings suggest that saliva RATs may be a useful option for mitigating SARS-CoV-2 transmission effectively. However, real-world variability in test sensitivity and sample collection should be carefully considered when evaluating the practical use of each RAT type. Our novel approach can be applied to other viruses and types of tests, enabling its use to inform public health policy decisions about which types of RAT to prioritize in future infectious disease epidemics.

Indexed as

Antigens, ViralCOVID-19COVID-19 Serological TestingAnimalsComputational BiologyHumansPandemicsRapid Diagnostic TestsSalivaSARS-CoV-2Antigens, Viral

Identifiers

PMID41926357
PMCPMC13082731

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.