Evidence map›Paper›PMID 34691239›Full record

ArticleComputational and mathematical methods in medicine2021

Improvement of Sensitivity of Pooling Strategies for COVID-19.

Hong-Bin Chen, Jun-Yi Guo, Yu-Chen Shu, Yu-Hsun Lee, Fei-Huang Chang

Abstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hong-Bin ChenDepartment of Applied Mathematics, National Chung Hsing University, Taichung 40249, Taiwan.ORCID https://orcid.org/0000-0001-9920-8802
Jun-Yi GuoDepartment of Mathematics, National Taiwan Normal University, Taipei 11677, Taiwan.ORCID https://orcid.org/0000-0003-2869-9143
Yu-Chen ShuDepartment of Mathematics, National Cheng Kung University, Tainan City 701, Taiwan.ORCID https://orcid.org/0000-0002-6450-7076
Yu-Hsun LeeGraduate School of Informatics, Kyoto University, Japan.ORCID https://orcid.org/0000-0002-4057-4055
Fei-Huang ChangDivision of Preparatory Programs for Overseas Chinese Students, National Taiwan Normal University, New Taipei City 24449, Taiwan.ORCID https://orcid.org/0000-0001-9038-747X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Group testing (or pool testing), for example, Dorfman's method or grid method, has been validated for COVID-19 RT-PCR tests and implemented widely by most laboratories in many countries. These methods take advantages since they reduce resources, time, and overall costs required for a large number of samples. However, these methods could have more false negative cases and lower sensitivity. In order to maintain both accuracy and efficiency for different prevalence, we provide a novel pooling strategy based on the grid method with an extra pool set and an optimized rule inspired by the idea of error-correcting codes. The mathematical analysis shows that (i) the proposed method has the best sensitivity among all the methods we compared, if the false negative rate (FNR) of an individual test is in the range [1%, 20%] and the FNR of a pool test is closed to that of an individual test, and (ii) the proposed method is efficient when the prevalence is below 10%. Numerical simulations are also performed to confirm the theoretical derivations. In summary, the proposed method is shown to be felicitous under the above conditions in the epidemic.

Indexed as

AlgorithmsComputer SimulationCOVID-19COVID-19 TestingFalse Negative ReactionsHumansLaboratoriesModels, TheoreticalPrevalenceProbabilityReproducibility of Results

Identifiers

PMID34691239
PMCPMC8528573

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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.