Monitoring occurrence of SARS-CoV-2 in school populations: A wastewater-based approach

  1. Victor Castro-Gutierrez
  2. Francis Hassard
  3. Milan Vu
  4. Rodrigo Leitao
  5. Beata Burczynska
  6. Dirk Wildeboer
  7. Isobel Stanton
  8. Shadi Rahimzadeh
  9. Gianluca Baio
  10. Hemda Garelick
  11. Jan Hofman
  12. Barbara Kasprzyk-Hordern
  13. Rachel Kwiatkowska
  14. Azeem Majeed
  15. Sally Priest
  16. Jasmine Grimsley
  17. Lian Lundy
  18. Andrew C. Singer
  19. Mariachiara Di Cesare

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Abstract

Clinical testing of children in schools is challenging, with economic implications limiting its frequent use as a monitoring tool of the risks assumed by children and staff during the COVID-19 pandemic. Here, a wastewater-based epidemiology approach has been used to monitor 16 schools (10 primary, 5 secondary and 1 post-16 and further education) in England. A total of 296 samples over 9 weeks have been analysed for N1 and E genes using qPCR methods. Of the samples returned, 47.3% were positive for one or both genes with a detection frequency in line with the respective local community. WBE offers a low cost, non-invasive approach for supplementing clinical testing and can provide longitudinal insights that are impractical with traditional clinical testing.

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  1. Version published to 10.1371/journal.pone.0270168
  2. ScreenIT

    SciScore for 10.1101/2021.03.25.21254231: (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

    No key resources detected.

    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:
    However, a school sample that reports LOD) but not quantifiable (

    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 10.1101/2021.03.25.21254231 on medRxiv