Nested pool testing strategy for the reliable identification of individuals infected with SARS-CoV-2

  1. Inés Armendáriz
  2. Pablo A. Ferrari
  3. Daniel Fraiman
  4. José M. Martínez
  5. Hugo G. Menzella
  6. Silvina Ponce Dawson

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Abstract

The progress of the SARS-CoV-2 pandemic requires the design of cost-effective testing programs at large scale. To this end, pooling multiple samples can provide a solution. Defining a cost-effective strategy requires the establishment of an efficient deconvolution and re-testing procedure that eventually allows the identifcation of the carrier. Based on Dorfman’s algorithm, we developed an adaptive nested strategy for which we have, for a given prevalence, simple analytic expressions of the optimal number of samples in the starting pool, of the number of partitioning steps (stages) in the optimal path, of the pool sizes in each of these stages and of the expected average number of tests needed to identify the infected individuals. In this paper we analyze the strategy in detail focusing on its practical implementation when there are restrictions that prevent the use of the optimum. More specifically, we analyze how to proceed when the infection prevalence is poorly known a priori or when the optimal requires starting with pool sizes that are too large for the reliable detection of an infected sample. The sensitivity of the RT-qPCR assay, the gold standard RNA detection method, is a major concern in the case of SARS-CoV-2: it is estimated that half of the infected individuals give false negative results. Recently, droplet digital PCR (ddPCR) was shown to be 10 − 100 times more sensitive than RT-qPCR, making this technology suitable for pool testing. ddPCR has the added value of providing the direct quantification of the RNA content at the end of the test. In the paper we show how this feature can be used for verification purposes. The analyses and strategies presented here should be useful to those considering the adoption of a pooling approach for RNA detection, particularly, for the identification of individuals infected with SARS-CoV-2.

Author summary

The progress of the SARS-CoV-2 pandemic requires the design of cost-effective testing programs at large scale. Running tests on pooled samples can provide a solution if the tests sensitivity is high enough. In the case of SARS-CoV-2, the current gold standard test, RT-qPCR, has shown some limitations that only allow the use of pools with relatively few samples. In this regard, Droplet digital PCR (ddPCR) has been shown to be 10 − 100 times more sensitive than RT-qPCR, making it suitable for test pooling. In this paper we describe a nested pool testing method in which the properties that make it optimal are simple analytic functions of the infection prevalence. We discuss how to proceed in practical implementations of the strategy, particularly when there are constraints that prevent the use of the optimal. We also show how its nested nature can be combined with the direct RNA quantification that the ddPCR test provides to identify the presence of unviable samples in the pools and for self-consistency tests. The studies of this paper should be useful for those considering the adoption of test pooling for RNA detection.

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    SciScore for 10.1101/2021.01.03.21249170: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    In particular, the position along the row (starting from 0) of the individual sample with subscripts is ik+1 + 3ik + … + 3k−1i2 + 3ki1.
    ik+1 + 3ik + … + 3k−1i2
    suggested: None

    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: We detected the following sentences addressing limitations in the study:
    This type of events have been shown to be very unlikely and of having a larger probability of false negatives is currently accepted as a limitation of the pooling method with no significant clinical meaning. Considering these limitations, test pooling may still serve for epidemiological purposes and for continuous validation of the method. As analyzed in this Section, however, accurate quantification that ddPCR provides can be of help to enlarge the truly confirmed set of negative tests. We discuss in what follows how a self-consistency check could be applied to the results obtained with our strategy when the nucleic acid content is quantified as described in S1 Appendix, Sec. 3, and how this quantification can be used to detect some of the flawed pooled samples that test negative. We also use the ddPCR quantification to determine the probability of detecting a single infected sample in a pool as a function of the pool size and the viral load. Test verification: In ddPCR the volume that goes in the reaction tube is subdivided into many (20,000) sub-volumes. At the end, the test gives (ideally) the number of sub-volumes that contained, at the beginning of the test, at least one molecule of the nucleic acid of interest (RNA in our case). As explained in S1 Appendix, Sec. 3, if the fraction of occupied sub-volumes is bounded away from 0 or 1, a range of possible values for the concentration of the RNA detected by the test can be obtained. As derived in S1 Appendix, Sec. 3, when ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • Thank you for including a protocol registration statement.

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  2. Version published to 10.1101/2021.01.03.21249170v1 on medRxiv