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Assessing the COVID-19 Vaccination Process via Functional Data Analysis

Abstract:

This manuscript aims to assess the evolution of the COVID-19 vaccination process in some American and European countries via Functional Data Analysis (FDA). Specifically, Functional Principal Components Analysis and Functional Clustering were implemented in a data set consisting of four COVID-19-related variables such as total cases per million, total deaths per million, total tests per thousand, and people fully vaccinated per hundred to explain heterogeneity in the vaccination process. We found that FDA methods are suitable to describe our study problem as, for example, the first two functional principal component corresponding to each variable explains above 96% of the variance. FDA techniques allow us to conclude that vaccines avoid people’s deaths from COVID-19, but they do not stop the propagation of the virus.