Abstract
AbstractIn December 2019 COVID-19 appeared as a new pandemic that has claimed the lives of millions of people around the world. This article presents a regional analysis of COVID-19 in Mexico. Due to the comorbidities of Mexican society, the new pandemic implies a higher risk for the population. The study period runs from April 12 to October 5, 2020 (761 665 Patients). In this proposal we apply 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 the Wigner semicircle law. The graphical presentation of the results is done with Machine Learning methods in the SuperHeat maps. With this is possible to analyze the behavior of patients who tested positive for COVID-19 and their comorbidities. We conclude that the most sensitive comorbidities in hospitalized patients are the following three: COPD, Other Diseases and Renal Diseases.
Publisher
Cold Spring Harbor Laboratory
Reference38 articles.
1. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health — The latest 2019 novel coronavirus outbreak in Wuhan, China
2. COVID-19 por SARS-CoV-2: la nueva emergencia de salud;Revista mexicana de pediatría,2019
3. WHO. Coronavirus. Retrieved July 3, 2021. From the World Health Organization. Available on: https://www.who.int/health-topics/coronavirus#tab=tab_1.
4. Spectral theory of random matrices;Russian Mathematical Surveys,1985
5. Melo, M. (2015). Applications of Random Matrices to Image Processing for Image Denoising. Available on: https://repository.lib.fit.edu/handle/11141/736