Tools for interpretation of wastewater SARS-CoV-2 temporal and spatial trends demonstrated with data collected in the San Francisco Bay Area

  1. Hannah D. Greenwald
  2. Lauren C. Kennedy
  3. Adrian Hinkle
  4. Oscar N. Whitney
  5. Vinson B. Fan
  6. Alexander Crits-Christoph
  7. Sasha Harris-Lovett
  8. Avi I. Flamholz
  9. Basem Al-Shayeb
  10. Lauren D. Liao
  11. Matt Beyers
  12. Daniel Brown
  13. Alicia R. Chakrabarti
  14. Jason Dow
  15. Dan Frost
  16. Mark Koekemoer
  17. Chris Lynch
  18. Payal Sarkar
  19. Eileen White
  20. Rose Kantor
  21. Kara L. Nelson

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Abstract

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  1. Version published to 10.1016/j.wroa.2021.100111
  2. ScreenIT

    SciScore for 10.1101/2021.05.04.21256418: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: 2.1 Wastewater sample collection and physicochemical data: 24-hour time-weighted composite samples of raw wastewater were collected using Teledyne ISCO autosamplers.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.6 Data analysis: All data analysis was performed in Python (v3.6.9) using key modules Pandas (v1.1.5), NumPy (v1.19.5), SciPy (v1.4.1), and Plotnine (v0.6.0).
    Python
    suggested: (IPython, RRID:SCR_001658)
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    SciPy
    suggested: (SciPy, RRID:SCR_008058)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite limitations in clinical testing data and the potential lag in wastewater trends, assessing correlations between clinical and wastewater testing data can help validate WBE (Xagoraraki and O’Brien, 2020). Moderate correlations with clinical data observed in this study (τ=0.43) support that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence. Wastewater data paired with clinical data can be a more robust public health surveillance strategy compared to either method alone, both for sewershed-scale and facility-scale surveillance applications. In some settings, wastewater testing may be a less resource-intensive way to implement population-scale surveillance, and policymakers will need to balance allocation of resources to each approach. A critical question for public health decision-making is how much early warning WBE can provide ahead of clinical testing, which could allow more timely public health responses to slow COVID-19 outbreaks. However, lead time is difficult to measure. Biologically, the time between onset of fecal shedding and nasal shedding is unclear (Benefield et al., 2020; Walsh et al., 2020). Practically, lead time depends on testing turnaround time and frequency of sampling for both wastewater and clinical testing. For example, clinical testing capabilities can increase the lead time of wastewater data if patients are only tested after symptom onset and can decrease the lead time if asymptomatic and symptomatic individual...

    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 10.1101/2021.05.04.21256418 on medRxiv