The brief comparison of the operational efficiency of pool-testing strategies for COVID-19 mass testing in PCR laboratories

  1. Kirill Vechera

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Abstract

This paper addresses the operational efficiency of different pool-testing strategies in typical scenarios of a PCR laboratory working in mass testing for COVID-19 with different values of prevalence, limitations and conditions of testing, and priorities of optimization.

The research employs a model of the laboratory’s testing process, created after interviewing of PCR laboratories and studying their operations. The limitations and operational characteristics of this model were applied in a simulation of the testing process with different pool-testing strategies managed by a computer program developed in the LOMT project.

The efficiency indicators assessed are the number of assays needed to obtain results of a batch of specimens, the number of specimens identified after the first analysis, and total time to obtain all results.

Depending on prevalence, constraints of testing, and priorities of optimization, different pool-testing strategies provide the best operational efficiency. The binary splitting algorithm provides the maximum reduction of the number of assays: from 1.99x reduction for a high prevalence (10%) to 25x reduction for a low prevalence (0.1%), while other algorithms provide the least amount of time to obtain results or the maximum number of the specimens classified after the first analysis.

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    SciScore for 10.1101/2020.07.14.20151415: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.07.14.20151415v1 on medRxiv