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

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.

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Publication Details

Journal
PLoS Computational Biology
Published
2026-04-02
DOI
https://doi.org/10.1371/journal.pcbi.1013102
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
0.00

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

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

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article en

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.

PLoS Computational BiologyVol. 22(4)
Los Alamos National Laboratory (US), Kyushu University (JP), University of Illinois Urbana-Champaign (US), Kyoto University (JP), University of Oxford (GB), National Center for Global Health and Medicine (JP), RIKEN (JP), Science Groove (Japan) (JP), Japanese Foundation For Cancer Research (JP), Pusan National University (KR), Nagoya University (JP), The University of Tokyo (JP), Pukyong National University (KR)
National Research Foundation of Korea, Japan Prize Foundation, Secom Science and Technology Foundation, Japan Society for the Promotion of Science, JST-Mirai Program, Moonshot Research and Development Program
Good health and well-being
Openalex Percentile: Top 30%
SARS-CoV-2 detection and testing
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