Impact Of Temperature and Sunshine Duration on Daily New Cases and Death due to COVID-19

  1. Swati Thangariyal
  2. Aayushi Rastogi
  3. Arvind Tomar
  4. Ajeet Bhadoria
  5. Sukriti Baweja

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Abstract

Background

The coronavirus pandemic (COVID-19) control has now become a critical issue for public health. Many ecological factors are proven to influence the transmission and survival of the virus. In this study, we aim to determine the association of different climate factors with the spread and mortality due to COVID-19.

Methods

The climate indicators included in the study were duration of sunshine, average minimum temperature and average maximum temperature, with cumulative confirmed cases, deceased and recovered cases. The data was performed for 138 different countries of the world, between January 2020 to May 2020. Both univariate and multivariate was performed for cumulative and month-wise analysis using SPSS software.

Results

The average maximum temperature, and sunshine duration was significantly associated with COVID-19 confirmed cases, deceased and recovered. For every one degree increase in mean average temperature, the confirmed, deceased and recovered cases decreased by 2047(p=0.03), 157(p=0.016), 743 (p=0.005) individuals. The association remained significant even after adjusting for environmental such as sunshine duration as well as non-environmental variables. Average sunshine duration was inveserly correlated with increase in daily new cases (ρ= -2261) and deaths (ρ= -0.2985).

Conclusion

Higher average temperature and longer sunshine duration was strongly associated with COVID-19 cases and deaths in 138 countries. Hence the temperature is an important factor in SARS CoV-2 survival and this study will help in formulating better preventive measures to combat COVID-19 based on their climatic conditions.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.13.20130138: (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
    Data Analysis: All the data were analyzed using the SPSS software version 22 software (IBM SPSS).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    Though our study is a unique study but there are several limitations in our study. The lacune of this study is that we assumed normal distribution of the outcome variable and linear relationship between exposure and outcome variable. However, the study was not able to address some kind of non-linearity in the analysis. Moreover, the data collected for the temperature and sunshine duration was only retrieved for the capital city and was assumed to be representative of the entire country and in reality it may not be the actual scenario. Since this study was retrospective, data for various variables was not available eg. humidity, airspeed, and most importantly the lockdown of human movement which is adopted by many countries. The total number of cases across the globe is difficult to predict due to the uncertainty of data collected by different countries. Hence, further evaluations are needed for a better understanding of the role of environmental and non environmental factors influencing the transmission of this pandemic. In conclusion, our study showed a possible association between environmental conditions and COVID-19 infection.

    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.

  2. Version published to 10.1101/2020.06.13.20130138v1 on medRxiv
  3. Version published to 10.4103/jfmpc.jfmpc_1185_20