UVA radiation could be a significant contributor to sunlight inactivation of SARS-CoV-2

  1. Paolo Luzzatto-Fegiz
  2. Fernando Temprano-Coleto
  3. François J. Peaudecerf
  4. Julien R. Landel
  5. Yangying Zhu
  6. Julie A. McMurry

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Abstract

Past experiments demonstrated SARS-CoV-2 inactivation by simulated sunlight; models have considered exclusively mechanisms involving UVB acting directly on RNA. However, UVA inactivation has been demonstrated for other enveloped RNA viruses, through indirect mechanisms involving the suspension medium. We propose a model combining UVB and UVA inactivation for SARS-CoV-2, which improves predictions by accounting for effects associated with the medium. UVA sensitivities deduced for SARS-CoV-2 are consistent with data for SARS-CoV-1 under UVA only. This analysis calls for experiments to separately assess effects of UVA and UVB in different media, and for including UVA in inactivation models.

Lay summary

Recent experiments have demonstrated that SARS-CoV-2 is inactivated by simulated sunlight; however, there are still many unknowns, including the mechanism of action and which part of the light spectrum is principally responsible. Our analysis indicates the need for targeted experiments that can separately assess the effects of UVA and UVB on SARS-CoV-2, and that sunlight inactivation models may need to be expanded to also include the effect of UVA. A first UVA-inclusive model is also proposed here. These findings have implications for how to improve the safety of the built environment, and for the seasonality of COVID-19.

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    SciScore for 10.1101/2020.09.07.286666: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For experiments that report infectious virion concentrations, nonlinear model fitting in MATLAB is used, on a logarithmic scale.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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.

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  2. Version published to 10.1101/2020.09.07.286666v1 on bioRxiv