Temperature Significantly Change COVID-19 Transmission in 429 cities

  1. Mao Wang
  2. Aili Jiang
  3. Lijuan Gong
  4. Lina Lu
  5. Wenbin Guo
  6. Chuyi Li
  7. Jing Zheng
  8. Chaoyong Li
  9. Bixing Yang
  10. Jietong Zeng
  11. Youping Chen
  12. Ke Zheng
  13. Hongyan Li

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Abstract

Background

There is no evidence supporting that temperature changes COVID-19 transmission.

Methods

We collected the cumulative number of confirmed cases of all cities and regions affected by COVID-19 in the world from January 20 to February 4, 2020, and calculated the daily means of the average, minimum and maximum temperatures in January. Then, restricted cubic spline function and generalized linear mixture model were used to analyze the relationships.

Results

There were in total 24,139 confirmed cases in China and 26 overseas countries. In total, 16,480 cases (68.01%) were from Hubei Province. The lgN rose as the average temperature went up to a peak of 8.72°C and then slowly declined. The apexes of the minimum temperature and the maximum temperature were 6.70°C and 12.42°C respectively. The curves shared similar shapes. Under the circumstance of lower temperature, every 1°C increase in average, minimum and maximum temperatures led to an increase of the cumulative number of cases by 0.83, 0.82 and 0.83 respectively. In the single-factor model of the higher-temperature group, every 1°C increase in the minimum temperature led to a decrease of the cumulative number of cases by 0.86.

Conclusion

The study found that, to certain extent, temperature could significant change COVID-19 transmission, and there might be a best temperature for the viral transmission, which may partly explain why it first broke out in Wuhan. It is suggested that countries and regions with a lower temperature in the world adopt the strictest control measures to prevent future reversal.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.02.22.20025791: (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
    Then, the generalized linear mixed model in SAS (version 9.4; SAS Institute Inc.) was developed to analyze the dose-response relationship.
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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:
    There are several limitations of the study. First, this is a time-space cross-sectional study. Thus, no causal relationship can be proved. But the nonlinear dose-response relationship was concluded. Second, official data regarding the imported cases could not be obtained, making it impossible to analyze the impact of the imported case numbers on the exponential function. However, a new variable, Day3, was adopted for the control analysis. Third, the impact of temperature on sex and age could not be analyzed because the key information was not available on the official release in many cities except for Hong Kong SAR. Thus, the cumulative number of cases calculated based on the daily confirmed cases was used. Epidemiologically, the impact of temperature varies between sexes and among different age groups and has obvious influence on the middle-aged and the aged populations. Despite of the limitations, the outcome of this study demonstrates statistical significance, consistency and novelty and has led to relatively clear conclusions.

    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. ScreenIT

    SciScore for 10.1101/2020.02.22.20025791: (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
    Then, the generalized linear mixed model in SAS (version 9.4; SAS Institute Inc.) was developed to analyze the dose-response relationship.
    SAS Institute
    suggested: (Statistical Analysis System, SCR_008567)

    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).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.02.22.20025791v1 on medRxiv