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 di with 30 cases and we fitted for 12 days, capturing thus the early exponential growth. We looked then for linear correlations of the exponents α 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 p-value: low Temperature (4⋅10-7), high ratio of old vs. working-age people (3⋅10-6), life expectancy (8⋅10-6), number of international tourists (1⋅10-5), earlier epidemic starting date di (2⋅10-5), high level of physical contact in greeting habits (6⋅10-5), lung cancer prevalence (6⋅10-5), obesity in males (1⋅10-4), share of population in urban areas (2⋅10-4), cancer prevalence (3⋅10-4), alcohol consumption (0.0019), daily smoking prevalence (0.0036), and UV index (0.004, 73 countries). We also find a correlation with low Vitamin D serum levels (0.002-0.006), but on a smaller sample, ∼50 countries, to be confirmed on a larger sample. There is highly significant correlation also with blood types: positive correlation with types RH- (3⋅10-5) and A+ (3⋅10-3), negative correlation with B+ (2⋅10-4). We also find positive correlation with moderate confidence level (p-value of 0.02∼0.03) with: CO2/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.