Application of Random Matrix Theory With Maximum Local Overlapping Semicircles for Comorbidity Analysis

Ponente(s): Oralia Nolasco Jáuregui, L. A. Quezada-Téllez, Y. Salazar-Flores, y Adán Díaz-Hernández.

This research presents a regional analysis of COVID-19 in Mexico. Because of comorbidities in Mexican society, this new pandemic puts the population at greater risk. The research period runs from April 12 to October 5, 2020 (761, 665 Patients). The main objective of this research is a description of the method with 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, which enabled analyzing the behavior of patients who tested positive for COVID-19 and their comorbidities, with the conclusion that the most sensitive comorbidities in hospitalized patients in the study period are the following three: COPD, Other Diseases, and Renal Diseases. Another objective is the daily behavior pandemic analysis; in this research, we have provided the graphical presentation of the results with Machine Learning methods employing Super Heat maps as a visual tool.