Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Joaquín Pérez-Ortega, Nelva Nely Almanza-Ortega, Kirvis Torres-Poveda, Gerardo Martínez-González, José Crispín Zavala-Díaz and Rodolfo Pazos-Rangel
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Joaquín Pérez-Ortega: Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Mexico
Nelva Nely Almanza-Ortega: Tecnológico Nacional de México/IT de Tlalnepantla, Tlalnepantla de Baz 54070, Mexico
Kirvis Torres-Poveda: Centro de Investigación Sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, Cuernavaca 62100, Mexico
Gerardo Martínez-González: Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Mexico
José Crispín Zavala-Díaz: Administración e Informática, Facultad de Contaduría, Universidad Autónoma de Morelos, Cuernavaca 62209, Mexico
Rodolfo Pazos-Rangel: Tecnológico Nacional de México/IT de Cd. Madero, Madero 89440, Mexico

Mathematics, 2022, vol. 10, issue 13, 1-16

Abstract: Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.

Keywords: clustering; COVID-19; Data Science; Data Science methodology; epidemiology; machine learning; pandemic; unsupervised learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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