We analyze risk factors correlated with the initial transmission growth rate of the  COVID-19 pandemic  . The number of cases follows an early exponential expansion; we chose as a starting point in each country the first day  with 30 cases and used 12 days  . We looked  for linear correlations of the exponents  with other variables, using 126 countries. We find a positive correlation with high C.L. with{\it   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  (2\cdot10^{-5}), high level of  contact in greeting habits (6 \cdot 10^{-5}), lung cancer  (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (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 levels ( p-value 0.002-0.006, smaller sample, \sim 50 countries  ). There is highly significant correlation also with blood type : positive correlation with  RH- ( 2 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}). We also find positive correlation with moderate C.L. (p-value of 0.02%DIFDELCMD < \sim0%%% .03) with: CO_2 emissions, type-1 diabetes, low vaccination coverage for Tuberculosis (BCG). Several such variables are correlated with each other and so they likely have common interpretations. 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.
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, 73 countries) . We also find a correlation with low Vitamin D levels ( 0.002-0.006, smaller sample, \sim 50 countries , to be confirmed on a larger sample ). There is highly significant correlation also with blood types : positive correlation with types RH- ( 3 \cdot10^{-5}) and A+ ( 3 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}). %DIFDELCMD < \sim0%%% Several of the above variables are intercorrelated and likely to have common interpretations. We performed a Principal Component Analysis, in order to find their significant independent linear combinations. We also analyzed a possible bias: countries with low GDP-per capita might have less testing and we discuss correlation with the above variables.
A
recent
A
in different countries
R
an early
in its early stages an almost
A
d_i
R
used
we fitted for
A
, capturing thus the early exponential growth
A
then
A
\alpha
R
using
for a sample of
R
with high C.L. with
,
A
i.e. faster spread of COVID-19
A
, with high confidence level with
A
d_i
A
physical
A
prevalence
R
urbanization
share of population in urban areas
D
smaller sample,
R
,
. We also find a correlation with
D
p-value
A
, to be confirmed on a larger sample
R
type
types
A
types
R
2
3
R
2
3
D
We also find positive correlation with moderate C.L. (p-value of 0.02
R
.03) with: CO_2 emissions, type-1 diabetes, low vaccination coverage for Tuberculosis (BCG). Several such variables are correlated with each other and so they likely
Several of the above variables are intercorrelated and likely to
R
also analyzed the possible existence of a
performed a Principal Component Analysis, in order to find their significant independent linear combinations. We also analyzed a possible
D
, typically located in warm regions,
D
intense