On the anti-correlation between COVID-19 infection rate and natural UV light in the UK

  1. Arnon Blum
  2. Constantina Nicolaou
  3. Ben Henghes
  4. Ofer Lahav

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

While it is well established that the rate of COVID-19 infections can be suppressed by social distancing, environmental effects may also affect it. We consider the hypothesis that natural Ultra-Violet (UV) light is reducing COVID-19 infections by enhancing human immunity through increasing levels of Vitamin-D and Nitric Oxide or by suppressing the virus itself. We focus on the United Kingdom (UK), by examining daily COVID-19 infections (F) and UV Index (UVI) data from 23 March 2020 to 10 March 2021. We find an intriguing empirical anti-correlation between log 10 (F) and log 10 (UVI) with a correlation coefficient of −0.934 from 11 May 2020 (when the first UK lockdown ended) to 10 March 2021. The anti-correlation may reflect causation with other factors which are correlated with the UVI. We advocate that UVI should be added as a parameter in modelling the pattern of COVID-19 infections and deaths. We started quantifying such correlations in other countries and regions.

Article activity feed

  1. Version published to 10.1101/2020.11.28.20240242v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.11.28.20240242: (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.

  3. Version published to 10.1101/2020.11.28.20240242v1 on medRxiv