High-Frequency, High-Throughput Quantification of SARS-CoV-2 RNA in Wastewater Settled Solids at Eight Publicly Owned Treatment Works in Northern California Shows Strong Association with COVID-19 Incidence

  1. Marlene K. Wolfe
  2. Aaron Topol
  3. Alisha Knudson
  4. Adrian Simpson
  5. Bradley White
  6. Duc J. Vugia
  7. Alexander T. Yu
  8. Linlin Li
  9. Michael Balliet
  10. Pamela Stoddard
  11. George S. Han
  12. Krista R. Wigginton
  13. Alexandria B. Boehm

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Abstract

Access to reliable, rapid monitoring data is critical to guide response to an infectious disease outbreak. For pathogens that are shed in feces or urine, monitoring wastewater can provide a cost-effective snapshot of transmission in an entire community via a single sample.

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  1. Version published to 10.1128/msystems.00829-21
  2. ScreenIT

    SciScore for 10.1101/2021.07.16.21260627: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: Samples were immediately stored at 4°C and transported to the lab where processing began immediately (within 6 hours of collection).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Thresholding was done using QuantaSoft™ Analysis Pro Software (Bio-Rad, version 1.0.596).
    QuantaSoft™ Analysis Pro
    suggested: None
    Statistical analysis: Statistics were computed using RStudio (version 1.1.1073).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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 10.1101/2021.07.16.21260627v1 on medRxiv