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COVID-19 transmission risk factors
  • Alessio Notari,
  • Giorgio Torrieri
Alessio Notari
University of Barcelona

Corresponding Author:[email protected]

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Giorgio Torrieri
State University of Campinas
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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, {\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
03 Apr 2022Published in Pathogens and Global Health volume 116 issue 3 on pages 146-177. 10.1080/20477724.2021.1993676