The effect of meteorological factors on extreme COVID-19 infection in Rwanda: The generalized additive extreme value modeling approach

Abstract

The novel human coronavirus disease, COVID-19, was first identified in China in 2019 and has since spread throughout the world becoming a global pandemic of great concern. High daily new cases have brought a heavy burden on health facilities and health workers helping patients and fighting the spread of this pandemic. Understanding the behavior of extreme cases of COVID-19 and associated factors is crucial to devise strategies to flatten the pandemic curve. This study used generalized additive modeling and extreme value theory approaches to analyze weekly maximum positive cases of COVID-19 together with three climate covariates (temperature, rainfall, and solar radiation) with the purpose to evaluate the predictive power of climate factors on extreme COVID-19 cases. According to the findings, a Generalized Extreme Value distribution with a constant location parameter, a linear model for the shape parameter with rainfall as a predictor, and a non-linear model for the scale parameter with temperature and rainfall as predictors fits the weekly maximum positive cases the best. As a result, both temperature and rainfall have a significant effect on the spread of the COVID-19 pandemic. The findings of this study make a significant contribution to the existing knowledge about the COVID-19 pandemic.