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

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.