COVID-19 transmission risk factors

We analyze risk factors correlated with the initial transmission growth rate of the recent COVID-19 pandemic in different countries. The number of cases follows in its early stages an almost exponential expansion; we chose as a starting point in each country the first day [Image: see text] with 30 cases and we fitted for 12 days, capturing thus the early exponential growth. We looked then for linear correlations of the exponents [Image: see text] with other variables, for a sample of 126 countries. We find a positive correlation, i.e. faster spread of COVID-19, with high confidence level with the following variables, with respective [Image: see text] -value: low Temperature ([Image: see text] ), high ratio of old vs. working-age people ([Image: see text] ), life expectancy ([Image: see text] ), number of international tourists ([Image: see text] ), earlier epidemic starting date [Image: see text] ([Image: see text] ), high level of physical contact in greeting habits ([Image: see text] ), lung cancer prevalence ([Image: see text] ), obesity in males ([Image: see text] ), share of population in urban areas ([Image: see text] ), cancer prevalence ([Image: see text] ), alcohol consumption ([Image: see text] ), daily smoking prevalence ([Image: see text] ), and UV index ([Image: see text] , 73 countries). We also find a correlation with low Vitamin D serum levels ([Image: see text] ), but on a smaller sample, [Image: see text] countries, to be confirmed on a larger sample. There is highly significant correlation also with blood types: positive correlation with types RH- ([Image: see text] ) and A+ ([Image: see text] ), negative correlation with B+ ([Image: see text] ). We also find positive correlation with moderate confidence level ([Image: see text] -value of [Image: see text] ) with: CO(2)/SO emissions, type-1 diabetes in children, low vaccination coverage for Tuberculosis (BCG). Several of the above variables are correlated with each other, and so they are likely to have common interpretations. We thus performed a Principal Component Analysis, to find the significant independent linear combinations of such variables. The variables with loadings of at least 0.3 on the significant PCA are: greeting habits, urbanization, epidemic starting date, number of international tourists, temperature, lung cancer, smoking, and obesity in males. We also analyzed the possible existence of a bias: countries with low GDP-per capita might have less intense testing, and we discuss correlation with the above variables.