COVID-19 Patients Analysis using Super Heat Map and Bayesian Network to Identify Comorbidities Correlations under Different Scenarios
*Corresponding Author: O Nolasco Jauregui, Department of Biostatics, Tecana American University, W Cypress Creek RD, Fort Lauderdale, Florida, USA, Email: oralianolasco.jauregui@gmail.com
Copyright: © 2021 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Given the exposure risk of comorbidities in Mexican society, the new pandemic involves the highest risk for the population in history. Objective: This article presents an analysis of the COVID-19 risk for the regions of Mexico. Method: The study period runs from April 12 to June 29, 2020 (220,667 patients). The method has an applied nature and according to its level of deepening in the object of study it is framed in a descriptive and explanatory analysis type. The data used here has a quantitative and semi-quantitative characteristic because they are the result of a questionnaire instrument made up of 34 fields and the virus test. The instrument is of a deliberate type. According to the manipulation of the variables, this research is a secondary type of practice, and it has a factual inference from an inductive method because it is emphasizing the concomitant variations for each region of the country. Results: Region 1 and Region 4 have a higher percentage of hospitalized patients, while Region 2 has a minimum of them. The average age of non-hospitalized patients is around 40 years old, while the hospitalized patients’ age is close to 55 years old. The most sensitive comorbidities in hospitalized patients are: obesity, diabetes mellitus and hypertension. The patients whose needed the mechanical respirator were in ranged from 7.45% to 10.79%. Conclusions: There is a higher risk of people losing their lives in the Region 1 and Region 4 territories than in Region 2, this information was dictated by the statistical analysis.
