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

Frontiers in Applied Mathematics and Statistics

Oralia Nolasco-Jáuregui¹  L. A. Quezada-Téllez²  Y. Salazar-Flores³  Adán Díaz-Hernández⁴ 

[1] Department of Biostatistics, Tecana American University, Fort Lauderdale, FL, United States;Escuela Superior de Apan, Universidad Autónoma del Estado de Hidalgo (UAEH), Chimalpa Tlalayote, Mexico;Facultad de Ciencias, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico;Facultad de Economía y Negocios, Universidad Anáhuac México, Huixquilucan, Mexico;

关键词: random matrix theory;    COVID-19;    Wigner's law;    multivariate distribution;    SuperHeat map;    eigenvalue distribution;   

DOI  :  10.3389/fams.2022.848898

来源: DOAJ

【 摘 要 】

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 12 April to 5 October 2020 761,665. This article 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.

【 授权许可】

Unknown