Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings

  1. Brian Cleary
  2. James A. Hay
  3. Brendan Blumenstiel
  4. Maegan Harden
  5. Michelle Cipicchio
  6. Jon Bezney
  7. Brooke Simonton
  8. David Hong
  9. Madikay Senghore
  10. Abdul K. Sesay
  11. Stacey Gabriel
  12. Aviv Regev
  13. Michael J. Mina

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Abstract

Simple group testing designs to improve SARS-CoV-2 surveillance in resource-constrained settings are identified using modeling and experimental data.

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  1. Version published to 10.1126/scitranslmed.abf1568
  2. ScreenIT

    SciScore for 10.1101/2020.05.01.20086801: (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
    Selecting pool compositions for large-scale sample identification validation: To form pools, we put each of the N = 96 individuals into q = 2 out of b = 6 pools (A-F) by cycling through the following ordered list of pool pairs: AB, CD, EF, BC, DF, AE, BD, AF, CE, BE, CF, AD, BF, DE, AC.
    AB
    suggested: RRID:BDSC_203)
    Software and Algorithms
    SentencesResources
    Fluorescence intensity is monitored at each PCR cycle by Applied Biosystems® ViiA7 Real-Time PCR System with QuantStudio version 1.3 software.
    QuantStudio
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our modeling results have a number of limitations and may be updated as more data become available. First, our simulation results depend on the generalizability of the simulated Ct values, which were based on viral load data from symptomatic patients. Although some features of viral trajectories, such as viral waning, differ between symptomatic and asymptomatic individuals, population-wide data suggest that the range of Ct values do not differ based on symptom status. (19, 35) Furthermore, we have assumed a simple hinge function to describe viral kinetics. Different shapes for the viral kinetics trajectory may become apparent as more data become available. Nonetheless, our simulated population distribution of Ct values is comparable to existing data and we propagated substantial uncertainty in viral kinetics parameters to generate a wide range of viral trajectories. For prevalence estimation, the MLE framework requires training on a distribution of Ct values. Such data can be available based on past tests from a given laboratory, but care should be taken to use a distribution appropriate for the population under consideration. For example, training the virus kinetics model on data skewed towards lower viral loads (as would be observed during the tail end of an epidemic curve) may be inappropriate when the true viral load distribution is skewed higher (as might be the case during the growth phase of an epidemic curve). Nevertheless, we used our simulated distribution of Ct, wh...

    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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  3. Version published to 10.1101/2020.05.01.20086801 on medRxiv