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

Carlos Martin-Barreiro, Xavier Cabezas, Víctor Leiva, Pedro Ramos De Santis, John A. Ramirez-Figueroa, Erwin J. Delgado

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)22693-22713
Number of pages21
JournalAIMS Mathematics
Issue number10
StatePublished - 2023


  • K-means analysis
  • R software
  • SARS-Cov2
  • clustering analysis
  • data science
  • disjoint PCA
  • multivariate statistical analysis
  • unsupervised methods


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