COVID-19 transmission risk factors

Abstract

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, i.e. faster spread of COVID-19, with high confidence level with the following variables, with respective $p$-value: low Temperature ($4\cdot {10}^{-7}$), high ratio of old vs. working-age people ($3\cdot {10}^{-6}$), life expectancy ($8\cdot {10}^{-6}$), number of international tourists ($1\cdot {10}^{-5}$), earlier epidemic starting date $d_i$ ($2\cdot {10}^{-5}$), 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 ($2\cdot {10}^{-4}$), cancer prevalence ($3\cdot {10}^{-4}$), 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, $\sim 50$ countries, to be confirmed on a larger sample. There is highly significant correlation also with blood types: positive correlation with types RH- ($3\cdot {10}^{-5}$) and A+ ($3\cdot {10}^{-3}$), negative correlation with B+ ($2\cdot {10}^{-4}$). We also find positive correlation with moderate confidence level ($p$-value of $0.02\sim 0.03$) 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.

Keywords: COVID-19; epidemiology; risk factors; statistical data.

Figure 1.

Exponent $\alpha$ for each country vs. GDP per capita. We show the data points and the best-fit for the linear interpolation.

Figure 2.

Exponent $\alpha$ for each country vs. average temperature T, for the relevant period of time, as defined in [1]. We show the data points and the best-fit for the linear interpolation.

Figure 3.

Exponent $\alpha$ for each country vs. old-age dependency ratio, as defined in the text. We show the data points and the best-fit for the linear interpolation.

Figure 4.

Exponent $\alpha$ for each country vs. life expectancy. We show the data points and the best-fit for the linear interpolation.

Figure 5.

Exponent $\alpha$ for each country vs. number of tourist arrivals. We show the data points and the best-fit for the linear interpolation.

Figure 6.

Exponent $\alpha$ for each country vs. starting date of the analysis of the epidemic, DATE, defined as the day when the positive cases reached $N=30$. Days are counted from 31 December 2019. We show the data points and the best-fit for the linear interpolation.

Figure 7.

Exponent $\alpha$ for each country vs. level of contact in greeting habits, $GRE$, as defined in the text. We show the data points and the best-fit for the linear interpolation.

Figure 8.

Exponent $\alpha$ for each country vs. lung cancer death rates. We show the data points and the best-fit for the linear interpolation.

Figure 9.

Exponent $\alpha$ for each country vs. prevalence of obesity in adult males. We show the data points and the best-fit for the linear interpolation.

Figure 10.

Exponent $\alpha$ for each country vs. share of population in urban areas. We show the data points and the best-fit for the linear interpolation.

Figure 11.

Exponent $\alpha$ for each country vs. prevalence of type-1 Diabetes (0–19 years). We show the data points and the best-fit for the linear interpolation.

Figure 12.

Exponent $\alpha$ for each country vs. alcohol consumption. We show the data points and the best-fit for the linear interpolation.

Figure 13.

Exponent $\alpha$ for each country vs. daily smoking prevalence. We show the data points and the best-fit for the linear interpolation.

Figure 14.

Exponent $\alpha$ for each country vs. UV index for the relevant month of the epidemic. We show the data points and the best-fit for the linear interpolation.

Figure 15.

Exponent $\alpha$ for each country vs. annual levels of vitamin $D$, for the relevant period of time, as defined in the text, for the base set of 42 countries. We show the data points and the best-fit for the linear interpolation.

Figure 16.

Exponent $\alpha$ for each country vs. seasonal levels of vitamin D, for the relevant period of time, as defined in the text, for the base set of 42 countries. We show the data points and the best-fit for the linear interpolation.

Figure 17.

Exponent $\alpha$ for each country vs. $\mathrm{C}{\mathrm{O}}_2$ emissions. We show the data points and the best-fit for the linear interpolation.

Figure 18.

Exponent $\alpha$ for each country vs. BCG vaccination coverage. We show the data points and the best-fit for the linear interpolation.

Figure 19.

Exponent $\alpha$ for each country vs. death rate from air pollution per 100,000. We show the data points and the best-fit for the linear interpolation.

Figure 20.

Exponent $\alpha$ for each country vs. share of women with high blood pressure. We show the data points and the best-fit for the linear interpolation.

Figure 21.

Exponent $\alpha$ for each country vs. incidence of Hepatitis B, for the relevant period of time, as defined in the text, for the base set of 42 countries. We show the data points and the best-fit for the linear interpolation.

Figure 22.

Exponent $\alpha$ for each country vs. prevalence of anemia in children. We show the data points and the best-fit for the linear interpolation.

Figure 23.

Exponent $\alpha$ for each country vs. percentage of population with blood type A+. We show the data points and the best-fit for the linear interpolation.

Figure 24.

Exponent $\alpha$ for each country vs. percentage of population with blood type B+. We show the data points and the best-fit for the linear interpolation.

Figure 25.

Exponent $\alpha$ for each country vs. percentage of population with blood type 0−. We show the data points and the best-fit for the linear interpolation.

Figure 26.

Exponent $\alpha$ for each country vs. percentage of population with blood type A−. We show the data points and the best-fit for the linear interpolation.

Figure 27.

Exponent $\alpha$ for each country vs. percentage of population with blood type B−. We show the data points and the best-fit for the linear interpolation.

Figure 28.

Exponent $\alpha$ for each country vs. percentage of population with blood type AB−. We show the data points and the best-fit for the linear interpolation.

Figure 29.

Exponent $\alpha$ for each country vs. percentage of population with RH-positive blood. We show the data points and the best-fit for the linear interpolation.

Figure 30.

Correlation coefficients between each variable in a pair. Such coefficient corresponds to the off-diagonal entry of the (normalized) covariance matrix, multiplied by $-1$. In the last column and row we show the $p$-value of each variable when performing a one-variable linear fit for the growth rate $\alpha$. Note also that the fits that include vitamin D variables (D and D_s) and UV index are based on smaller samples than for the other fits and were collected with rather with inhomogeneous data, and so have to be confirmed on a larger sample, as explained in the text. The variables considered here are: Temperature (T), Old age dependency ratio (OLD), Life expectancy (LIFE), Number of tourist arrivals (ARR), Starting date of the epidemic (DATE), Amount of contact in greeting habits (GRE), Lung cancer (LUNG), Obesity in males (OBE), Urbanization (URB), UV Index (UV), GDP per capita (GDP), Alcohol consumption (ALCO), Daily smoking prevalence (SMOK), Prevalence of anemia in children (ANE), Death rate due to pollution (POLL), Prevalence of hepatitis B (HEP), High blood pressure in females (PRE), average vitamin D serum levels (D), seasonal vitamin D serum levels (D_s), CO₂ emissions (CO${ }_2$), type 1 diabetes prevalence (DIAB), BCG vaccination (BCG), percentage with blood of RH+ type (RH+), and percentage with blood type B+ (B+).

Figure 31.

Significance and $R^2$ for linear fits of $\alpha$ as a function of two-variables. Variables names are the same as in Figure 30. Each cell in the table gives $R^2$ and the $p$-value of the each of the two variables, using $t$-statistic, labeled as p${ }_H$ for horizontal and p${ }_V$ for vertical.