Quantitative analysis of SARS-CoV-2 RNA from wastewater solids in communities with low COVID-19 incidence and prevalence

  1. Patrick M. D'Aoust
  2. Elisabeth Mercier
  3. Danika Montpetit
  4. Jian-Jun Jia
  5. Ilya Alexandrov
  6. Nafisa Neault
  7. Aiman Tariq Baig
  8. Janice Mayne
  9. Xu Zhang
  10. Tommy Alain
  11. Marc-André Langlois
  12. Mark R. Servos
  13. Malcolm MacKenzie
  14. Daniel Figeys
  15. Alex E. MacKenzie
  16. Tyson E. Graber
  17. Robert Delatolla

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Abstract

No abstract available

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  1. Version published to 10.1016/j.watres.2020.116560
  2. Version published to 10.1101/2020.08.11.20173062v3 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.08.11.20173062: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Positive droplets were called manually, and absolute quantification was performed using Quantasoft Analysis Pro v.1.0 (Bio-Rad, Hercules, CA).
    Quantasoft Analysis Pro
    suggested: None
    Statistical analysis: In order to test for significant differences between data sets comparing the detection of SARS-CoV-2 in PGS and PCS samples, chi-square and Fisher’s exact test statistical analyses were conducted using GraphPad’s Prism 8.3 software (La Jolla, CA).
    Prism
    suggested: (PRISM, RRID:SCR_005375)

    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:
    Two key factors/limitations are noted with respect to these epidemiological data sets shown in this study. Firstly, the testing at the onset of the pandemic (March and April 2020) was variable and low in both cities due to limitations in human resources, laboratory reagents and testing equipment. Hence, the first four weeks of the twelve-week period for which wastewater samples were profiled were subject to variable and lower testing rates per day that likely under-reported both the number of new cases and active cases during this period. Secondly, early testing/screening was less available to the general population in both cities, with testing heavily biased towards hospitalized patients and health care workers. This potentially artificially inflates the percent positive data during the first four weeks of the study, with the effect on the percent positive being likely lesser than the effect of limited testing on the total case numbers. SARS-CoV-2 RNA in PCS and correlation with COVID-19 case data: The average and standard deviation of technical triplicates and extraction replicates that repeated the concentration, extraction and RT-qPCR steps (shown as error bars in Figures 6 and 7) for the longitudinal viral RNA data sets in this study are plotted along with a percent positive and seven day floating average percent positive epidemiological data sets. Due to limited testing during the first four weeks of the longitudinal study, percent positive was identified as a potential...

    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

    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.

  4. Version published to 10.1101/2020.08.11.20173062v1 on medRxiv
  5. Version published to 10.1101/2020.08.11.20173062v2 on medRxiv