Comparisons of different statistical models for analyzing the effects of meteorological factors on COVID-19

Abstract

Objective: This general non-systematic review aimed to gather information on reported statistical models examing the effects of meteorological factors on coronavirus disease 2019 (COVID-19) and compare these models. Methods: PubMed, Web of Science, and Google Scholar were searched for studies on "meteorological factors and COVID-19" published between January 1, 2020, and October 1, 2022. Results: The most commonly used approaches for analyzing the association between meteorological factors and COVID-19 were the linear regression model (LRM), generalized linear model (GLM), generalized additive model (GAM), and distributed lag non-linear model (DLNM). In addition to these classical models commonly applied in environmental epidemiology, machine learning techniques are increasingly being used to select risk factors for the outcome of interest and establishing robust prediction models. Conclusion: Selecting an appropriate model is essential before conducting research. To ensure the reliability of analysis results, it is important to consider including non-meteorological factors (e.g., government policies on physical distancing, vaccination, and hygiene practices) along with meteorological factors in the model.

Keywords

coronavirus disease 2019 / meteorological factors / general coronavirus disease 2019 / meteorological factors / general

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