Detection of SARS-CoV-2 RNA throughout wastewater treatment plants and a modeling approach to understand COVID-19 infection dynamics in Winnipeg, Canada

  1. Kadir Yanaç
  2. Adeola Adegoke
  3. Liqun Wang
  4. Miguel Uyaguari
  5. Qiuyan Yuan

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Abstract

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  1. Version published to 10.1016/j.scitotenv.2022.153906
  2. ScreenIT

    SciScore for 10.1101/2021.10.26.21265146: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The model was rewritten in RStudio since the original script was written in MATLAB.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    Lack of information regarding population and COVID-19 infection dynamics in each sewershed is one of the limitations of this study, preventing us from doing a sewershed-based analysis. Peak concentrations of SARS-CoV-2 were observed on the day when critical-level restrictions were enforced and the following 4th and 6th day (Fig. 4). Other observed concentrations after the first detection were generally in close proximity with the modeled concentrations for at least one genomic target. Different decay rates for N1 and N2 resulted in higher concentrations of N1 for back-trajectory modeled samples collected between November 16th and December 15th, while the difference between N1 and N2 concentrations for the rest of the samples was smaller than 0.35 on log10 scale (Fig. 4). The decay rate of N1 was calculated based on the degradation of genomic signals of gamma-irradiated SARS-CoV-2 by Ahmed et al. (2020b) and had a higher standard deviation of 15% and lower R2 of 0.79 compared to those values of N2, which were based on degradation of active SARS-CoV-2 (Hokajärvi et al., 2021a) (Table 2). Gamma irradiation of SARS-CoV-2 and relatively higher variations in the decay rate of N1 might result in significant biases in such a back-trajectory modeling approach. Therefore, we mostly consider N2 concentrations for back-trajectory modeled samples in the discussion. The critical level restrictions in Manitoba were applied when extensive community transmission of COVID-19 occurred, outbreak...

    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.

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

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.10.26.21265146v1 on medRxiv