Wastewater-Based Epidemiology for COVID-19: Handling qPCR Nondetects and Comparing Spatially Granular Wastewater and Clinical Data Trends

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Abstract

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  1. SciScore for 10.1101/2022.01.28.22269911: (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

    Software and Algorithms
    SentencesResources
    Because closed forms for the posterior distribution do not exist for this application, we sampled from the posterior using MCMC via Python’s Stan package (pystan).
    Python’s
    suggested: (PyMVPA, RRID:SCR_006099)
    MATLAB® software (version R2021a; MathWorks) was used for subsequent analysis.
    MATLAB®
    suggested: (MATLAB, RRID:SCR_001622)
    Values in between sampling dates were linearly interpolated to facilitate comparison of wastewater and clinical data, and the MATLAB “smoothdata” function was applied using a centered 7-day moving average.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    We designed a Python tool (available at https://tinyurl.com/Safford-et-al-Predictive) that combines information on municipal wastewater flows with U.S. Census Bureau data to probabilistically assign HDT asymptomatic testing results to sewershed sampling zones via three steps.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    While the Spearman correlation analysis provides a framework for interpreting the data, it suffers from several limitations. These include factors discussed above, including the low COVID-19 burden in Davis, imprecision associated with the predictive probability model, and external factors that can inherently confound wastewater analysis. It is also hard to definitively “match” trends in clinical and wastewater data. For instance, trends in clinical data collected from symptomatic individuals been observed to lag trends in wastewater data [21,22]. But it is unknown whether and to what extent this lag may apply when clinical data derives from a large-scale asymptomatic testing program like HDT. With these caveats in mind, important takeaways from the correlation analysis are as follows. First, because the LOD0.5 and Ctmax methods involve a similar approach, their correlation coefficients track more closely with each other than with the Ctavg or multiple imputation coefficients. Second, much higher correlation coefficients were generally observed for the 11 zones where wastewater surveillance began prior to the winter COVID-19 surge. This can be explained by greater activity in the wastewater and clinical data during the winter surge, as well as by the fact that sampling zones added later in the campaign were generally smaller—and hence less active— than zones added earlier. Time periods and zones with more data activity provide more positive data points on which to perform mea...

    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.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.


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