SARS-CoV-2 and other respiratory pathogens are detected in continuous air samples from congregate settings

  1. Mitchell D. Ramuta
  2. Christina M. Newman
  3. Savannah F. Brakefield
  4. Miranda R. Stauss
  5. Roger W. Wiseman
  6. Amanda Kita-Yarbro
  7. Eli J. O’Connor
  8. Neeti Dahal
  9. Ailam Lim
  10. Keith P. Poulsen
  11. Nasia Safdar
  12. John A. Marx
  13. Molly A. Accola
  14. William M. Rehrauer
  15. Julia A. Zimmer
  16. Manjeet Khubbar
  17. Lucas J. Beversdorf
  18. Emma C. Boehm
  19. David Castañeda
  20. Clayton Rushford
  21. Devon A. Gregory
  22. Joseph D. Yao
  23. Sanjib Bhattacharyya
  24. Marc C. Johnson
  25. Matthew T. Aliota
  26. Thomas C. Friedrich
  27. David H. O’Connor
  28. Shelby L. O’Connor

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Abstract

Two years after the emergence of SARS-CoV-2, there is still a need for better ways to assess the risk of transmission in congregate spaces. We deployed active air samplers to monitor the presence of SARS-CoV-2 in real-world settings across communities in the Upper Midwestern states of Wisconsin and Minnesota. Over 29 weeks, we collected 527 air samples from 15 congregate settings. We detected 106 samples that were positive for SARS-CoV-2 viral RNA, demonstrating that SARS-CoV-2 can be detected in continuous air samples collected from a variety of real-world settings. We expanded the utility of air surveillance to test for 40 other respiratory pathogens. Surveillance data revealed differences in timing and location of SARS-CoV-2 and influenza A virus detection. In addition, we obtained SARS-CoV-2 genome sequences from air samples to identify variant lineages. Collectively, this shows air sampling is a scalable, high throughput surveillance tool that could be used in conjunction with other methods for detecting respiratory pathogens in congregate settings.

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  1. Version published to 10.1038/s41467-022-32406-w
  2. ScreenIT

    SciScore for 10.1101/2022.03.29.22272716: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Ct values were exported and analyzed in Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Reads that fully contain the desired amplicon sequence were extracted with the bedtools (v2.30.0) intersect tool.
    bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    Next, a consensus sequence was generated by first generating a pileup with the samtools mpileup tool using default settings and then generating a consensus with ivar (v1.3.1) using the parameters ‘-q 20 -t 0 -m 20’.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Samples were kept frozen at -70°C until total nucleic extraction was performed using 200 µL elute and Applied Biosystems™ MagMAX™
    Applied Biosystems™
    suggested: (Applied Biosystems, RRID:SCR_005039)

    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.
    • 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 10.1101/2022.03.29.22272716v1 on medRxiv