Rapid and low‐cost sampling for detection of airborne SARS‐CoV‐2 in dehumidifier condensate

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Abstract

Airborne spread of coronavirus disease 2019 (COVID‐19) by infectious aerosol is all but certain. However, easily implemented approaches to assess the actual environmental threat are currently unavailable. We present a simple approach with the potential to rapidly provide information about the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in the atmosphere at any location. We used a portable dehumidifier as a readily available and affordable tool to collect airborne virus in the condensate. The dehumidifiers were deployed in selected locations of a hospital ward with patients reporting flu‐like symptoms which could possibly be due to COVID‐19 over three separate periods of one week. Samples were analyzed frequently for both virus envelope protein and SARS‐CoV‐2 RNA. In several samples across separate deployments, condensate from dehumidifiers tested positive for the presence of SARS‐CoV‐2 antigens as confirmed using two independent assays. RNA was detected, but not attributable to SARS‐CoV‐2. We verified the ability of the dehumidifier to rapidly collect aerosolized sodium chloride. Our results point to a facile pool testing method to sample air in any location in the world and assess the presence and concentration of an infectious agent to obtain quantitative risk assessment of exposure, designate zones as “hot spots” and minimize the need for individual testing which may often be time consuming, expensive, and laborious.

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  1. SciScore for 10.1101/2020.10.08.20208785: (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: 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.

    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.