COVID-19 Social Lethality Characterization in some Regions of Mexico through the Pandemic Years Using Data Mining

Abstract

In this chapter, an analysis of the data provided by the Federal Government of Mexico related to the COVID-19 disease during the pandemic years is described. For this study, nineteen significant variables were considered, which included the test result for detecting the presence of the SARS-CoV-2 virus, the alive/deceased people cases, and different comorbidities that affect a person’s health such as diabetes, hypertension, obesity, and pneumonia, among other variables. Thus, based on the KDD (Knowledge Discovery in Databases) process and data mining techniques, we undertook the task of preprocessing such data to generate classification models for identifying patterns in the data or correlations among the different variables that could have influence on COVID-19 deaths. The models were generated by using different classification algorithms, were selected based on a high correct classification rate, and were validated with the help of the cross-validation test. In this way, the period corresponding to the five SARS-CoV-2 infection waves that occurred in Mexico between March 2020 and October 2022 was analyzed with the main purpose of characterizing the COVID-19 social lethality in the most contagious regions of Mexico.