论文标题
COVID-19传输风险因素
COVID-19 transmission risk factors
论文作者
论文摘要
我们分析了风险因素与不同国家最近Covid-19的初始传播增长率相关。在早期阶段的案件数量几乎是指数级的扩展；我们选择在每个国家的第一天起点$ d_i $，其中30个案例，我们安装了12天，从而捕获了早期指数增长。然后，我们查找了指数$α$与其他变量的线性相关性，用于126个国家的样本。我们发现正相关，{\ it，即covid-19}的更快传播，具有以下变量的较高置信度，各自的$ p $ - 值：低温（$ 4 \ cdot10^{ - 7} $），旧VS. （$ 8 \ cdot10^{ - 6} $），国际游客数量（$ 1 \ cdot10^{ - 5} $），早期的流行启动日期$ d_i $（$ 2 \ cdot10^{ - 5} $），高水平的物理接触，迎接habits中的高水平10^{ - 5} $），男性肥胖（$ 1 \ cdot 10^{ - 4} $），城市地区的人口份额（$ 2 \ cdot10^{ - 4} $），癌症患病率（$ 3 \ cdot 10^{ - 4} （$ 0.004 $，73个国家）。我们还发现与低维生素D水平（$ 0.002-0.006 $，较小的样本，$ \ sim 50 $国家 /地区的国家 /地区）的相关性。与血液类型也有非常重要的相关性：与rh-类型的正相关（$ 3 \ cdot10^{ - 5} $）和a+（$ 3 \ cdot10^{ - 3} $），与b+（$ 2 \ cdot10^{ - 4} $）负相关。以上几个变量相互相关，并且可能具有共同的解释。我们进行了主成分分析，以找到其重要的独立线性组合。我们还分析了可能的偏见：Per Capita较低的国家的测试可能较少，我们讨论了与上述变量的相关性。
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 $α$ 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+ ($2\cdot10^{-4}$). 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.
