SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases

  1. Fuqing Wu
  2. Amy Xiao
  3. Jianbo Zhang
  4. Katya Moniz
  5. Noriko Endo
  6. Federica Armas
  7. Richard Bonneau
  8. Megan A. Brown
  9. Mary Bushman
  10. Peter R. Chai
  11. Claire Duvallet
  12. Timothy B. Erickson
  13. Katelyn Foppe
  14. Newsha Ghaeli
  15. Xiaoqiong Gu
  16. William P. Hanage
  17. Katherine H. Huang
  18. Wei Lin Lee
  19. Mariana Matus
  20. Kyle A. McElroy
  21. Jonathan Nagler
  22. Steven F. Rhode
  23. Mauricio Santillana
  24. Joshua A. Tucker
  25. Stefan Wuertz
  26. Shijie Zhao
  27. Janelle Thompson
  28. Eric J. Alm

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Abstract

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  1. Version published to 10.1016/j.scitotenv.2021.150121
  2. ScreenIT

    SciScore for 10.1101/2020.06.15.20117747: (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
    s.lowess in python 3.6.5 and statsmodels 0.9.0.
    python
    suggested: (IPython, RRID:SCR_001658)
    Correlation analysis and multiple hypothesis correction were done with python 3.6.5, numpy 1.14.3, pandas 0.23.0, scipy 1.1.0, and statsmodels 0.9.0.
    scipy
    suggested: (SciPy, RRID:SCR_008058)

    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:
    This difference between wastewater and clinical data could be due to underdiagnosis of asymptomatic or mildly symptomatic cases, limitations in clinical testing capacity, or a time delay between viral shedding and the onset of respiratory and other symptoms. We tested the correlation of viral titers with new clinical cases and cumulative cases, allowing for a variable time lag (Figure 1C/D and Figure S2). Higher correlations were seen when comparing new clinical cases back-dated by 4-10 days than for shorter or longer temporal offsets, and the maximum agreement between the two time series was observed for a time offset of 4 days (Pearson’s r = 0.92, p = 4.17e-15, Figure 1C/D). Similar results were found when each catchment was considered individually (Figure S3). This time lag between the wastewater signal and clinically reported cases is consistent with the typical 4-5 day incubation period from SARS-CoV-2 infection to symptom onset (14-16). Moreover, a short time lag in an exponentially growing process explains large differences in the total number of cases (e.g. 2 cases as of March 3, compared to 108 cases as of March 12). Thus, wastewater surveillance could potentially be used to predict trends in new COVID-19 cases. An inferred viral shedding function suggests an early burst of high viral shedding: The high correlation between wastewater viral titers and daily reports of new clinically confirmed COVID-19 cases (Figure 1D) – combined with the time lag between the wastewat...

    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.

  3. Version published to 10.1101/2020.06.15.20117747v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.06.15.20117747: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Correlation analysis and multiple hypothesis correction were done with python 3.6.5, numpy 1.14.3, pandas 0.23.0, scipy 1.1.0, and statsmodels 0.9.0.
    python
    suggested: (IPython, SCR_001658)
          <div style="margin-bottom:8px">
        <div><b>scipy</b></div>
        <div>suggested: (SciPy, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008058">SCR_008058</a>)</div>
      </div>
    </td></tr></table>

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    • This difference between wastewater and clinical data could be due to underdiagnosis of asymptomatic or mildly symptomatic cases, limitations in clinical testing capacity, or a time delay between viral shedding and the onset of respiratory and other symptoms.

    Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  5. Version published to 10.1101/2020.06.15.20117747v1 on medRxiv