论文标题
该国感染和死亡率的差异的相关性在COVID-19大流行中的第一波：贝叶斯模型的证据平均
Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: Evidence from Bayesian model averaging
论文作者
论文摘要
在COVID-19大流行的最初浪潮中，我们观察到国家之间感染和死亡率的差异很大。除了生物学和流行病学因素外，许多社会和经济标准还影响了这些差异出现的程度。因此，关于关键的社会经济和健康因素存在与大流行的感染和死亡率结果相关的重要辩论。在这里，我们利用贝叶斯模型平均技术和国家级别的数据来研究28个变量的潜力，描述了各种各样的健康和社会经济特征，是在冠状病毒大流行期间第一波感染和死亡的最终数量和死亡的相关性。我们表明，只有很少的变量能够与这些结果牢固相关。为了了解潜在的关系在解释感染和死亡率方面的关系，我们创建了一个联合空间。使用这个空间，我们得出结论，每个变量能够在多大程度上为COVID-19感染/死亡率结果提供可靠的解释，因为它们的异质特征因其异质性而有所不同。
In the initial wave of the COVID-19 pandemic we observed great discrepancies in both infection and mortality rates between countries. Besides the biological and epidemiological factors, a multitude of social and economic criteria also influence the extent to which these discrepancies appear. Consequently, there is an active debate regarding the critical socio-economic and health factors that correlate with the infection and mortality rates outcome of the pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate the potential of 28 variables, describing a diverse set of health and socio-economic characteristics, in being correlates of the final number of infections and deaths during the first wave of the coronavirus pandemic. We show that only few variables are able to robustly correlate with these outcomes. To understand the relationship between the potential correlates in explaining the infection and death rates, we create a Jointness Space. Using this space, we conclude that the extent to which each variable is able to provide a credible explanation for the COVID-19 infections/mortality outcome varies between countries because of their heterogeneous features.
