Early Detection of Severe Acute Respiratory Syndrome Coronavirus 2 Variants Using Traveler-based Genomic Surveillance at 4 US Airports, September 2021–January 2022

  1. Renee D Wegrzyn
  2. Grace D Appiah
  3. Robert Morfino
  4. Scott R Milford
  5. Allison Taylor Walker
  6. Ezra T Ernst
  7. William W Darrow
  8. Siyao Lisa Li
  9. Keith Robison
  10. Duncan MacCannell
  11. Dongjuan Dai
  12. Brintha P Girinathan
  13. Allison L Hicks
  14. Bryan Cosca
  15. Gabrielle Woronoff
  16. Alex M Plocik
  17. Birgitte B Simen
  18. Leah Moriarty
  19. Sarah Anne J Guagliardo
  20. Martin S Cetron
  21. Cindy R Friedman

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Abstract

We enrolled arriving international air travelers in a severe acute respiratory syndrome coronavirus 2 genomic surveillance program. We used molecular testing of pooled nasal swabs and sequenced positive samples for sublineage. Traveler-based surveillance provided early-warning variant detection, reporting the first US Omicron BA.2 and BA.3 in North America.

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  1. Version published to 10.1093/cid/ciac461
  2. ScreenIT

    SciScore for 10.1101/2022.03.21.22272490: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Participants were 18 years or older, provided informed consent, and completed demographic, clinical, and travel history questions.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    With SARS-CoV-2 genome MN908947.3 as reference, Illumina data was aligned with BWA-MEM and Guppy-called Nanopore data with minimap2 (using ont-map option)
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)
    Variant Call Format (VCF) files were generated with FreeBayes [4] (Illumina) or medaka-variant (Nanopore) and these VCF files used in both cases to generate consensus sequences with bcftools.
    FreeBayes
    suggested: (FreeBayes, RRID:SCR_010761)

    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 10.1101/2022.03.21.22272490v1 on medRxiv