Rapid, dose-dependent and efficient inactivation of surface dried SARS-CoV-2 by 254 nm UV-C irradiation

  1. Natalia Ruetalo
  2. Ramona Businger
  3. Michael Schindler

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background: The COVID-19 pandemic urges for cheap, reliable, and rapid technologies for disinfection and decontamination. One frequently proposed method is ultraviolet (UV)-C irradiation. UV-C doses necessary to achieve inactivation of high-titre SARS-CoV-2 are poorly defined.

Aim: We investigated whether short exposure of SARS-CoV-2 to UV-C irradiation sufficiently reduces viral infectivity and doses necessary to achieve an at least 6-log reduction in viral titres.

Methods: Using a box and two handheld systems designed to decontaminate objects and surfaces, we evaluated the efficacy of 254 nm UV-C treatment to inactivate surface dried high-titre SARS-CoV-2.

Results: Drying for 2 hours did not have a major impact on the infectivity of SARS-CoV-2, indicating that exhaled virus in droplets or aerosols stays infectious on surfaces for at least a certain amount of time. Short exposure of high titre surface dried virus (3–5*10^6 IU/ml) with UV-C light (16 mJ/cm 2 ) resulted in a total inactivation of SARS-CoV-2. Dose-dependency experiments revealed that 3.5 mJ/cm 2 were still effective to achieve a > 6-log reduction in viral titres, whereas 1.75 mJ/cm 2 lowered infectivity only by one order of magnitude.

Conclusions: SARS-CoV-2 is rapidly inactivated by relatively low doses of UV-C irradiation and the relationship between UV-C dose and log-viral titre reduction of surface residing SARS-CoV-2 is nonlinear. Our findings emphasize that it is necessary to assure sufficient and complete exposure of all relevant areas by integrated UV-C doses of at least 3.5 mJ/cm 2 at 254 nm. Altogether, UV-C treatment is an effective non-chemical option to decontaminate surfaces from high-titre infectious SARS-CoV-2.

Article activity feed

  1. Version published to 10.2807/1560-7917.es.2021.26.42.2001718
  2. Version published to 10.1101/2020.09.22.308098v3 on bioRxiv
  3. Version published to 10.1101/2020.09.22.308098v2 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.09.22.308098: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    To generate icSARS-CoV-2-mNG stocks, 200,000 Caco-2 cells were infected with 50 μl of virus stock in a 6-well plate, the supernatant was harvested 48 hpi, centrifuged, and stored at −80°C.
    Caco-2
    suggested: None
    Software and Algorithms
    SentencesResources
    For quantification of infection rates, images were taken with the Cytation3 (Biotek) and Hoechst+ and mNG+ cells were automatically counted by the Gen5 Software (Biotek).
    Gen5
    suggested: (Gen5, RRID:SCR_017317)
    Software and statistical analysis: GraphPad Prism 8.0 was used for statistical analyses and to generate graphs.
    GraphPad Prism
    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: 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.

  5. Version published to 10.1101/2020.09.22.308098v1 on bioRxiv