Wastewater SARS-CoV-2 monitoring as a community-level COVID-19 trend tracker and variants in Ohio, United States

  1. Yuehan Ai
  2. Angela Davis
  3. Dan Jones
  4. Stanley Lemeshow
  5. Huolin Tu
  6. Fan He
  7. Peng Ru
  8. Xiaokang Pan
  9. Zuzana Bohrerova
  10. Jiyoung Lee

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Abstract

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  1. Version published to 10.1016/j.scitotenv.2021.149757
  2. ScreenIT

    SciScore for 10.1101/2021.06.08.21258421: (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
    All statistical analyses were performed in RStudio using “ggpubr”, “stats”, and “rstatix” packages.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    Analysis tools include custom pipelines utilizing GATK and Mutect2 (Broad Institute) and Dragen SARS-COVID variant detection (Illumina).
    GATK
    suggested: (GATK, RRID:SCR_001876)
    NGS was performed with CovidSeq as above or using the AmpliSeq SARS-CoV-2 Research Panel on Ion Chef-S5 instruments (Thermofisher, Waltham, MA).
    CovidSeq
    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: 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.

    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 10.1101/2021.06.08.21258421v1 on medRxiv