The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain)

TL;DR

This study investigates how different statistical modeling choices affect the inferred relationship between environmental factors and COVID-19 spread in Catalonia, emphasizing the importance of appropriate spatio-temporal models for understanding pandemic dynamics.

Contribution

The paper demonstrates the significant impact of modeling choices on COVID-19 environmental association results and highlights the value of spatio-temporal models in pandemic analysis.

Findings

Model choice greatly influences the inferred environmental effects.

Spatio-temporal models reveal pandemic evolution in space and time.

Contradictory results in literature may stem from different modeling approaches.

Abstract

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 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 is analysed. 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.