The choices that researchers make while conducting a statistical analysis usually have a notable impact on the results. This fact has become evident in the ongoing research of the association between the environment and the evolution of the coronavirus disease 2019 (COVID-19) pandemic, in light of the hundreds of contradictory studies that have already been published on this issue in just a few months. In this paper, a COVID-19 dataset containing the number of daily cases registered in the regions of Catalonia (Spain) since the start of the pandemic to the end of August 2020 is analysed using statistical models of diverse levels of complexity. Specifically, the possible effect of several environmental variables (solar exposure, mean temperature, and wind speed) on the number of cases is assessed. Thus, the first objective of the paper is to show how the choice of a certain type of statistical model to conduct the analysis can have a severe impact on the associations that are inferred between the covariates and the response variable. Secondly, it is shown how the use of spatio-temporal models accounting for the nature of the data allows understanding the evolution of the pandemic in space and time. The results suggest that even though the models fitted to the data correctly capture the evolution of COVID-19 in space and time, determining whether there is an association between the spread of the pandemic and certain environmental conditions is complex, as it is severely affected by the choice of the model.

研究人员在进行统计分析时所做的选择通常会对结果产生显着影响。这一事实在正在进行的环境与 2019 年冠状病毒病 (COVID-19) 大流行演变之间的关联研究中变得显而易见，因为在短短几篇文章中已经发表了数百项关于该问题的相互矛盾的研究个月。在本文中，使用不同复杂程度的统计模型分析了一个 COVID-19 数据集，该数据集包含自大流行开始至 2020 年 8 月末在加泰罗尼亚（西班牙）地区登记的每日病例数。具体来说，评估了几个环境变量（日照、平均温度和风速）对病例数量的可能影响。因此，本文的第一个目标是展示选择某种类型的统计模型来进行分析如何对协变量和响应变量之间推断的关联产生严重影响。其次，它展示了如何使用时空模型来解释数据的性质，从而了解大流行在空间和时间上的演变。结果表明，即使拟合数据的模型正确地捕捉到了 COVID-19 在空间和时间上的演变，但确定大流行的传播与某些环境条件之间是否存在关联是复杂的，因为它受到严重影响模型的选择。