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An Exploratory Analysis of COVID-19 in Latin America Using Functional Principal Component Analysis

Abstract:

Even though there already exists a wide variety of epidemiological models, it’s worthwhile to apply Functional Data Analysis (FDA) techniques to study the shapes of the COVID-19 pandemic in Latin America. In the present work we use Functional Principal Component Analysis (FPCA) to make an exploratory study on a dataset formed by the total cases per million, new cases, new tests, and stringency index of 6 Latin American countries, namely: Mexico, Ecuador, Chile, Peru, Cuba, and Colombia; obtained from the first confirmed case reported to January 2021, measured daily. We identify an increasing pattern in all of the variables and the interesting case of Cuba concerning the management of the pandemic, as well as the influence of stringency index over the growth curve of positive cases, and the mean perturbations with functional principal components (FPC) of the variables. Finally, we suggest more FDA techniques to carry out further studies to get a broad perspective of COVID-19 in Latin America.