The pandemic caused by the SARS-CoV-2 virus spreads more rapidly in densely populated areas. The number of confirmed cases is counted by the millions in some countries, such as USA, Brazil, and Mexico. These three countries also report the world’s highest cumulative death tolls caused by the disease as of February 2021. In this study, a comparative analysis of urban development, economic level, and the number of COVID-19 cases in Mexico City, is conducted. Mexico City, the capital city of Mexico, is among the most densely populated metropolitan areas and one of the largest financial centers in the continent. Among the sixteen municipalities, in which Mexico City is divided, there exist enormous economic and urban development gaps. Based in a comparability index (CI), this study found a correlation between the number of confirmed cases of the COVID-19 disease with the population density, the per capita income, and the dwelling occupancy index in each municipality.

Background. Given the exposure risk of comorbidities in Mexican society, the new pandemic involves the highest risk for the population in the history. Objective. This article presents an analysis of the COVID-19 risk from Mexico's regions. Method. The study period runs from April 12 to June 29, 2020 (220,667 patients). The method has a nature applied 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 practices, 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 it is close to 55 years. The most sensitive comorbidities in hospitalized patients are three principal: 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 lose their lives in the Region 1 and Region 4 territories than in the Region 2, this information was dictated by the statistical analysis.

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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.

In 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.