Application of Random Matrix Theory With Maximum Local Overlapping Semicircles for Comorbidity Analysis - Nolasco-Jáuregui, Oralia; Quezada-Téllez, L. A.; Salazar-Flores, Y.; Díaz-Hernández, Adán (Frontiers in Applied Mathematics and Statistics, 2022)
In December 2019, the COVID-19 pandemic began, which has claimed the lives of millions of people around the world. This article presents a regional analysis of COVID-19 in Mexico. Due to comorbidities in Mexican society, this new pandemic implies a higher risk for the population. The study period runs from April 12 to October 5, 2020 (761, 665 Patients). This paper proposes a unique methodology of random matrix theory in the moments of a probability measure that appears as the limit of the empirical spectral distribution by Wigner's semicircle law. The graphical presentation of the results is done with Machine Learning methods in the SuperHeat maps. With this, it was possible to analyze the behavior of patients who tested positive for COVID-19 and their comorbidities, with the conclusion that the most sensitive comorbidities in hospitalized patients are the following three: COPD, Other Diseases, and Renal Diseases.