Coronavirus surveillance of wildlife in the Lao People’s Democratic Republic detects viral RNA in rodents

  1. David J. McIver
  2. Soubanh Silithammavong
  3. Watthana Theppangna
  4. Amethyst Gillis
  5. Bounlom Douangngeun
  6. Kongsy Khammavong
  7. Sinpakone Singhalath
  8. Veasna Duong
  9. Philippe Buchy
  10. Sarah H. Olson
  11. Lucy Keatts
  12. Amanda E. Fine
  13. Zoe Greatorex
  14. Martin Gilbert
  15. Matthew LeBreton
  16. Karen Saylors
  17. Damien O. Joly
  18. Edward M. Rubin
  19. Christian E. Lange

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Abstract

Coronaviruses can become zoonotic as in the case of COVID-19, and hunting, sale, and consumption of wild animals in Southeast Asia facilitates an increased risk for such incidents. We sampled and tested rodents (851) and other mammals, and found Betacoronavirus RNA in 12 rodents. The sequences belong to two separate genetic clusters, and relate closely to known rodent coronaviruses detected in the region, and distantly to human coronaviruses OC43 and HKU1. Considering close human-wildlife contact with many species in and beyond the region, a better understanding of virus diversity is urgently needed for the mitigation of future risks.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.22.056218: (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: Thank you for sharing your data.

    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.
    • No funding statement was detected.
    • 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.

  2. Version published to 10.1101/2020.04.22.056218 on bioRxiv