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Background. The burden of the coronavirus disease 2019 (COVID-19) pandemic has been geographically disproportionate. Certain weather factors and population characteristics are thought to drive transmission, but studies examining these factors are limited. We aimed to identify weather, sociodemographic, and geographic drivers of COVID-19 at the global scale using a comprehensive collection of country/territory-level data, and to use discovered associations to estimate the timing of community transmission. Methods. We examined COVID-19 cases and deaths reported up to May 2, 2020 across 205 countries and territories in relation to weather data collected from capital cities for the eight weeks prior to and four weeks after the date of the first reported case, as well as country/territory-level population, geographic, and planetary data. We performed univariable and multivariable regression modeling and odds ratio analyses to investigate associations with COVID-19 cases, deaths, and epidemic growth rate. We also conducted maximum likelihood analysis to estimate the timing of initial community spread. Findings. Lower temperature (p |
