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 $d_i$ with 30
cases and we fitted for 12 days, capturing thus the early exponential
growth. We looked then for linear correlations of the exponents
$\alpha$ with other variables, for a sample of 126
countries. We find a positive correlation, {\it i.e.
faster spread of COVID-19}, with high confidence level with the
following variables, with respective $p$-value: low Temperature
($4\cdot10^{-7}$), high ratio of old
vs.~working-age people
($3\cdot10^{-6}$), life expectancy
($8\cdot10^{-6}$), number of international
tourists ($1\cdot10^{-5}$), earlier epidemic
starting date $d_i$ ($2\cdot10^{-5}$), high
level of physical contact in greeting habits ($6 \cdot
10^{-5}$), lung cancer prevalence ($6 \cdot
10^{-5}$), obesity in males ($1 \cdot
10^{-4}$), share of population in urban areas
($2\cdot10^{-4}$), cancer prevalence ($3
\cdot 10^{-4}$), alcohol consumption ($0.0019$),
daily smoking prevalence ($0.0036$), UV index ($0.004$, smaller
sample, 73 countries), low Vitamin D serum levels ($0.002-0.006$,
smaller sample, $\sim 50$ countries). There is highly
significant correlation also with blood type: positive correlation with
types RH- ($3\cdot10^{-5}$) and A+
($3\cdot10^{-3}$), negative correlation with B+
($2\cdot10^{-4}$). We also find positive
correlation with moderate confidence level ($p$-value of
$0.02\sim0.03$) 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 likely
to have common interpretations. We thus performed a Principal Component
Analysis, in order to find the significant independent linear
combinations of such variables. We also analyzed the possible existence
of a bias: countries with low GDP-per capita, typically located in warm
regions, might have less intense testing and we discuss correlation with
the above variables