Meteorological Conditions and Covid-19 in Large U.S. Cities

  1. Hisato Takagi
  2. Toshiki Kuno
  3. Yujiro v
  4. Hiroki Ueyama
  5. Takuya Matsushiro
  6. Yosuke Hari
  7. Tomo Ando

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Abstract

To determine whether prevalence of Coronavirus disease 2019 (Covid-19) is modulated by meteorological conditions, we herein conducted meta-regression of data in large U.S. cities. We selected 33 large U.S. cities with a population of >500,000. The integrated numbers of confirmed Covid-19 cases in the country to which the city belongs on 14 May 2020, the estimated population in 2019 in the country, and monthly meteorological conditions at the city for 4 months (from January to April 2020) were obtained. Meteorological conditions consisted of mean temperature (F), total precipitation (inch), mean wind speed (MPH), mean sky cover, and mean relative humidity (%). Monthly data for 4 months were averaged or integrated. The Covid-19 prevalence was defined as the integrated number of Covid-19 cases divided by the population. Random-effects meta-regression was performed by means of OpenMetaAnalyst. In a meta-regression graph, Covid-19 prevalence (plotted as the logarithm transformed prevalence on the y-axis) was depicted as a function of a given factor (plotted as a meteorological datum on the x-axis). A slope of the meta-regression line was significantly negative (coefficient, -0.069; P < 0.001) for the mean temperature and significantly positive for the mean wind speed (coefficient, 0.174; P = 0.027) and the sky cover (coefficient, 2.220; P = 0.023). In conclusion, lower temperature and higher wind speed/sky cover may be associated with higher Covid-19 prevalence, which should be confirmed by further epidemiological researches adjusting for various risk and protective factors (in addition to meteorological conditions) of Covid-19.

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    SciScore for 10.1101/2020.05.17.20104547: (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
    We selected 33 large U.S. cities with a population of >500,000 in 2010 from the U.S. Census Bureau (http://www.census.gov).
    http://www.census.gov
    suggested: (U.S. Census Bureau, RRID:SCR_011587)

    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.

  2. Version published to 10.1101/2020.05.17.20104547v1 on medRxiv