About: 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 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 $0.02/sim0.03$) 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.   Goto Sponge  NotDistinct  Permalink

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  • 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 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 $0.02/sim0.03$) 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.
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  • Senescence
  • Gaseous signaling molecules
  • Sun tanning
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