The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.

在这项工作中，通过基于 D-vine copula 的分位数回归研究了 COVID-19 在意大利传播的主要决定因素。结果是 2020 年 10 月 30 日登记的 COVID-19 累积感染率，参考意大利 107 个省份，并根据考虑医疗、环境和人口因素的一些利益协变量进行回归。为了处理空间自相关问题，基于 D-vine copula 的分位数回归还嵌入了一个空间自回归分量，用于控制空间依赖的程度。使用 vine copula 增强了模型的灵活性，以解决非线性关系和尾部依赖关系。此外，模型选择过程会导致简约模型根据其对结果的解释能力提供协变量等级。