Statistical characterization of vaccinated cases and deaths due to COVID-19: methodology and case study in South America

Abstract

<abstract><p>Many studies have been performed in different regions of the world as a result of the COVID-19 pandemic. In this work, we perform a statistical study related to the number of vaccinated cases and the number of deaths due to COVID-19 in ten South American countries. Our objective is to group countries according to the aforementioned variables. Once the groups of countries are built, they are characterized based on common properties of countries in the same group and differences between countries that are in different groups. Countries are grouped using principal component analysis and K-means analysis. These methods are combined in a single procedure that we propose for the classification of the countries. Regarding both variables, the countries were classified into three groups. Political decisions, availability of resources, bargaining power with suppliers and health infrastructure among others are some of the factors that can affect both the vaccination process and the timely care of infected people to avoid death. In general, the countries acted in a timely manner in relation to the vaccination of their citizens with the exception of two countries. Regarding the number of deaths, all countries reached peaks at some point in the study period.</p></abstract>

On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders

“…The application of ML and statistical classification techniques spans various areas of medicine and molecular sciences [52][53][54][55][56][57][58][59][60][61][62]. Numerous studies have been conducted with the aim of identifying factors that can accurately and early on indicate signs of heart disease [63,64].…”