Combining Community Wastewater Genomic Surveillance with State Clinical Surveillance: A Framework for SARS-CoV-2 Public Health Practice

  1. Ted Smith
  2. Rochelle H. Holm
  3. Ray Yeager
  4. Joseph B. Moore
  5. Eric C. Rouchka
  6. Kevin J. Sokoloski
  7. Erin M. Elliott
  8. Daymond Talley
  9. Vaneet Arora
  10. Sarah Moyer
  11. Aruni Bhatnagar

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Abstract

This study aimed to develop a framework for combining community wastewater surveillance with state clinical surveillance for the confirmation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants within the community and to provide recommendations on how to expand on such research and apply the findings in public health responses. Wastewater samples were collected weekly from 17 geographically resolved locations in Louisville/Jefferson County, Kentucky (USA), from February 10 to December 13, 2021. Genomic surveillance and quantitative reverse transcription PCR (RT-qPCR) platforms were used to screen for SARS-CoV-2 in wastewater, and state clinical surveillance was used for confirmation. The study results highlighted an increased epidemiological value of combining community wastewater genomic surveillance and RT-qPCR with conventional case-auditing methods. The spatial scale and temporal frequency of wastewater sampling provided promising sensitivity and specificity for gaining public health screening insights about SARS-CoV-2 emergence, seeding, and spread in communities. Improved national surveillance systems are needed against future pathogens and variants, and wastewater-based genomic surveillance exhibits great potential when coupled with clinical testing. This paper presents evidence that complementary wastewater and clinical testing are cost-effectively enhanced when used in combination, as they provide a strong tool for a joint public health framework. Future pathogens of interest may be examined in either a targeted fashion or using a more global approach where all pathogens are monitored. This study has also provided novel insights developed from evidence-based public health practices.

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  1. Version published to 10.1007/s12560-022-09531-2
  2. ScreenIT

    SciScore for 10.1101/2021.12.06.21267150: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: Wastewater sample collection and handling: Wastewater was collected as a 24-hour time-weighted composite sample at 17 sites in Jefferson County, Kentucky, USA, from February 10 to November 29, 2021.
    IRB: Results of this analysis were available within the same week the sample was collected and were shared with public county and state public health officials. 2.3. Ethics: The University of Louisville Institutional Review Board classified this project as Non-Human Subjects Research (reference #: 717950).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    As of week 40 (November 22, 2021 sampling date), a total of N=646 samples have been sequenced using NGS approaches.
    NGS
    suggested: (PM4NGS, RRID:SCR_019164)
    The sequences were then directly aligned to the SARS-CoV-2 reference genome assembly (NC_045512.2) using BWA (Li 2013).
    BWA
    suggested: (BWA, RRID:SCR_010910)
    Reads were then trimmed using Swift Biosciences primerclip (Addetia et al. 2020) to remove the primer read regions to avoid variant calling within these regions.
    Swift
    suggested: (Swift, RRID:SCR_013018)

    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 10.1101/2021.12.06.21267150v1 on medRxiv