Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States

  1. Claire Duvallet
  2. Fuqing Wu
  3. Kyle A. McElroy
  4. Maxim Imakaev
  5. Noriko Endo
  6. Amy Xiao
  7. Jianbo Zhang
  8. Róisín Floyd-O’Sullivan
  9. Morgan M. Powell
  10. Samuel Mendola
  11. Shane T. Wilson
  12. Francis Cruz
  13. Tamar Melman
  14. Chaithra Lakshmi Sathyanarayana
  15. Scott W. Olesen
  16. Timothy B. Erickson
  17. Newsha Ghaeli
  18. Peter Chai
  19. Eric J. Alm
  20. Mariana Matus

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Abstract

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  1. Version published to 10.1021/acsestwater.1c00434
  2. ScreenIT

    SciScore for 10.1101/2021.09.08.21263283: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Data analysis: We excluded locations representing fewer than 5000 people in their catchment population to reduce data variability, and locations which did not consent to data sharing.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Cts were called from raw fluorescence data using the Cy0 algorithm from the qpcR package (v1.4-1) in R (Guescini et al., 2008), and manually inspected for agreement with the raw traces in the native BioRad Maestro software.
    BioRad Maestro
    suggested: None
    Statistical analyses and visualizations were performed in Python 3.8.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has several limitations. First, wastewater SARS-CoV-2 concentrations are highly variable due to sampling, lab processing, and qPCR analysis. We attempted to reduce this challenge through normalization with a fecal virus with biological similarities to SARS-CoV-2 (Wu et al., 2020) and by optimizing our lab protocols (Methods). However, we still observed large spikes in wastewater concentrations in some locations which were not explained by changes in reported COVID-19 cases. These data challenges are common in the field of wastewater-based epidemiology, and our group and others are actively developing models to correct for variability and better interpret spikes. Second, the geographic comparisons between wastewater results and reported cases are not exact, as sampling location catchments may represent a subset or superset of the respective comparison counties (Table S1). However, we found no relationship between a catchment’s coverage of its comparison county and its correlation between wastewater and cases, indicating that these wastewater catchments were sufficient to identify broad and general trends in their associated communities (Fig S4). Alternative public health applications of wastewater-based epidemiology like building-level or manhole sampling may require more granular insight, in which geographies may need to be more precisely aligned. Third, wastewater data showed a range of correlation strengths with reported cases (Fig 1, Fig S3). In addition to techn...

    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 10.1101/2021.09.08.21263283v1 on medRxiv