End-to-End Protocol for the Detection of SARS-CoV-2 from Built Environments

  1. Ceth W. Parker
  2. Nitin Singh
  3. Scott Tighe
  4. Adriana Blachowicz
  5. Jason M. Wood
  6. Arman Seuylemezian
  7. Parag Vaishampayan
  8. Camilla Urbaniak
  9. Ryan Hendrickson
  10. Pheobe Laaguiby
  11. Kevin Clark
  12. Brian G. Clement
  13. Niamh B. O’Hara
  14. Mara Couto-Rodriguez
  15. Daniela Bezdan
  16. Christopher E. Mason
  17. Kasthuri Venkateswaran

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Abstract

The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (the virus responsible for coronavirus disease 2019 [COVID-19]) pandemic has led to a global slowdown with far-reaching financial and social impacts. The SARS-CoV-2 respiratory virus is primarily transmitted from person to person through inhalation of infected droplets or aerosols. However, some studies have shown that virions can remain infectious on surfaces for days and can lead to human infection from contact with infected surfaces. Thus, a comprehensive study was conducted to determine the efficiency of protocols to recover SARS-CoV-2 from surfaces in built environments. This end-to-end study showed that the effective combination for monitoring SARS-CoV-2 on surfaces required a minimum of 1,000 viral particles per 25 cm 2 to successfully detect virus from surfaces. This comprehensive study can provide valuable information regarding surface monitoring of various materials as well as the capacity to retain viral RNA and allow for effective disinfection.

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  1. Version published to 10.1128/msystems.00771-20
  2. ScreenIT

    SciScore for 10.1101/2020.08.16.20172668: (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
    Statistical Analyses: All statistical analyses were performed using GraphPad Prism Version 8.2.0
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad Software, San Diego, California USA).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    Unlike clinical samples, fomites and high-touch surfaces that become contaminated with the virus display lower concentrations of the virus (10), which are often difficult to detect due to method limitations and, in some cases, inhibitory materials. For this reason, robust methods are imperative for the recovery and detection of SARS-CoV-2 from environmental surfaces. Previous studies have analyzed a variety of methods for viral recovery from surfaces (11); however, there are substantial number of variables that can impact collection, processing, and quantification of viral particles. Despite the World Health Organizations “How To” guide for SARS-CoV-2 surface sampling in hospital settings, there has not been a comprehensive study that adequately addresses all the issues associated with an E2E assay for SARS-CoV-2. During this study, Isohelix swabs were selected over Copan swabs due to easier handling and higher sensitivity for sample collection, and this approach has been successfully used by other studies (12). Furthermore, our results demonstrated that automated RNA extraction was as efficient (13, 14) as manual kits for extracting synthetic SARS-CoV-2, which has been previously noted using phenol-chloroform (15). At the outset of this study, in February 2020, there were several molecular methods available for assaying the virus in a given sample. Various reports demonstrated well-established techniques, such as RT-qPCR (16), RT-LAMP (1), polyA RNA-seq (17), ribo-depletion ...

    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.

  3. Version published to 10.1101/2020.08.16.20172668v1 on medRxiv