The effect of ambient temperature on worldwide COVID-19 cases and deaths – an epidemiological study

  1. Anver Sethwala
  2. Mohamed Akbarally
  3. Nathan Better
  4. Jeffrey Lefkovits
  5. Leeanne Grigg
  6. Huzefa Akbarally

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Abstract

Background

The role of ambient temperature in the spread of SARS-CoV-2 infections and subsequent deaths due to COVID-19 remains contentious. Coronaviruses such as the 2003 SARS-CoV showed an increased risk of transmission during cooler days. We sought to analyse the effects of ambient temperature on SARS-COV-2 transmission and deaths related to the virus.

Methods

The world population of COVID-19 cases and attributable deaths from the 23 rd January 2020 to 11 th April 2020 were analysed. Temperature 5 days before cases and 23 days prior to deaths (to account for the time lag of incubation period and time from symptoms to death) was compared to the average temperature experienced by the world population.

Results

The total number of cases during this period was 1,605,788 and total number of deaths was 103,471. The median temperature at the time of COVID-19 infection was 9.12°C (10–90 th percentile 4.29–17.97°C) whilst the median temperature of the world population for the same period was 9.61°C warmer at 18.73°C (10–90 th percentile 4.09-28.49°C) with a notional p-value =5.1 x10 −11 . The median temperature at the time of a COVID-19 death was 9.72°C (10–90 th percentile 5.39–14.11°C) whilst the median temperature of the world population was 7.55°C warmer at 17.27°C (10–90 th percentile 2.57°C-27.76°C) with a notional p-value = 1.1 x10 −10 . 80% of all COVID-19 related cases and deaths occurred between 4.29°C and 17.97°C.

Conclusion

A definitive association between infection rate and death from COVID-19 and ambient temperature exists, with the highest risk occurring around 9°C. Governments should maintain vigilance with containment strategies when the ambient temperatures correspond to this highest risk.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.15.20102798: (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
    9 The results were collated using Python Software® and graphing done using Matplotlib (J. D. Hunter, “Matplotlib: A 2D Graphics Environment”
    Python
    suggested: (IPython, RRID:SCR_001658)
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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

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  2. Version published to 10.1101/2020.05.15.20102798v1 on medRxiv