Estimate of airborne transmission of SARS-CoV-2 using real time tracking of health care workers

  1. Bala Hota
  2. Brian Stein
  3. Michael Lin
  4. Alex Tomich
  5. John Segreti
  6. Robert A. Weinstein

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Abstract

BACKGROUND

Whether and to what degree SARS-CoV-2 is spread via the airborne route is unknown. Using data collected from health care worker interactions with hospitalized patients with COVID-19 illness, we calculated the transmissibility of SARS-CoV-2 via the airborne route.

OBJECTIVES/METHODS

Healthcare worker interaction with SARS-CoV-2 infected patients were tracked using a real time location system between March 18 and March 31. A value for q, the transmissibility expressed as quanta per hour, was estimated using a well-established model for airborne transmission.

RESULTS

SARS-CoV-2 infection prevalence among tracked HCWs was 2.21% (0.07-4.35). Transmissibility was estimated to be 0.225 quanta per hour, well below other well-characterized airborne pathogens. Simulations demonstrated that risk of infection is substantially reduced with increased ventilation of rooms.

CONCLUSIONS

Overall, our findings suggest that SARS-CoV-2 is not well transmitted via the airborne route in controlled conditions. We speculate that SARS-CoV-2 may be only opportunistically airborne, with most transmission occurring via droplet methods.

One Sentence Summary

We calculated the airborne transmissibility (q) of SARS-CoV-2 and the impact of masks and ventilation in a hospital setting.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.15.20154567: (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

    Software and Algorithms
    SentencesResources
    Zenodo. http://doi.org/10.5281/zenodo.3934582 Materials and Methods: Patients or participants: Nurses caring for patients with COVID-19 infection between March 18 and March 31, 2020.
    Zenodo
    suggested: (ZENODO, RRID:SCR_004129)

    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:
    Our study is subject to several limitations. First, we do not have detailed data about what types of masks were used by healthcare workers in their interactions with patients. While a policy to use surgical masks was in place, it is possible healthcare workers may have used N95 masks, and we modeled this as well. Second, we could not fully account for acquisition of SARS-CoV-2 infection through community exposures. We did model in our analyses the rates of community onset infection through the use of observed rates among individuals without patient care exposure. We could not account for potential spread among healthcare workers in non-clinical settings such as break rooms in which close contact between individuals may occur(39) or superspreader events or aerosol generating procedures. Third, while we measured healthcare worker-patient interactions in this cohort using the RTLS system, exposures among healthcare workers that aren’t tracked using this system (e.g. physicians) could not be assessed. Fourth, these results are applicable to hospitalized patients with COVID-19; risks in the community may be different. Our modeling did not account for the use of masks, especially surgical masks, by infected individuals, though in practice, inpatients did not wear facemasks unless they were outside their private rooms. Fifth, we did not culture asymptomatic people during this time and our estimates of SARS-CoV-2 prevalence may be underestimates among both non-clinical and clinical h...

    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.

  2. ScreenIT

    SciScore for 10.1101/2020.07.15.20154567: (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

    Software and Algorithms
    SentencesResources
    Zenodo. http://doi.org/10.5281/zenodo.3934582 Supplementary Materials: Materials and Methods Fig 1.
    Zenodo
    suggested: (ZENODO, SCR_004129)
    Detection of SARS-CoV-2 RNA for employees and patients was based upon the real-time PCR amplification and detection of genomic RNA obtained from nasopharyngeal swab testing at the Rush microbiology using RealTime SARS-CoV-2 assay (Abbott Laboratories).
    Abbott Laboratories
    suggested: None
    All analyses were conducted in the anaconda distribution of python 3.7.
    python
    suggested: (IPython, SCR_001658)

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.07.15.20154567 on medRxiv