Modelar la incidencia de la infección por COVID-19 en el área metropolitana de Santiago de Cali, en términos de variables socioeconómicas, demográficas y de salud, usando métodos estadísticos, de econometría espacial y machine learning, en el periodo comprendido de marzo 2020 - junio 2021

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dc.contributor.advisorBohorquez Castañeda, Martha Patricia
dc.contributor.authorSaenz Perilla, Juan Pablo
dc.coverage.cityCali
dc.coverage.temporal2021 - 2021
dc.date.accessioned2023-08-31T20:57:02Z
dc.date.available2023-08-31T20:57:02Z
dc.date.issued2023-02
dc.descriptionilustraciones, diagramas, mapasspa
dc.description.abstractEl objetivo de este documento es modelar la incidencia del COVID-19 en Cali en términos de los factores socioeconómicos, demográficos y de salud de los contagiados. Se estiman modelos lineales generalizados de Poisson y Regresión de Poisson Ponderada geográficamente. Además, Se ajustan y evalúan técnicas de Aprendizaje automático, y se usan los algoritmos Bosque Aleatorio (Random Forest); Potenciación del Gradiente Extremo (eXtreme Gradient Boosting); Red Neuronal (Neural Network) y Máquinas de Vectores de Soporte (Support Vector Machine). Finalmente, en todos los casos se incluye el componente espacial. Se seleccionan las variables más influyentes con base en la correlación y en la técnica de regularización Lasso. Se encuentra que ciertas afecciones de salud preexistentes (comorbilidades), el tipo de vacuna, la edad, y el régimen de salud están asociados significativamente con los casos de COVID-19 por barrio en la ciudad de Cali. (Texto tomado de la fuente)spa
dc.description.abstractThe objective of this document is to model the incidence of COVID-19 in Cali in terms of the socioeconomic, demographic, and health factors of those infected. Generalized Poisson linear models and geographically weighted Poisson Regression models are employed. Additionally, machine learning techniques are applied, including Random Forest, eXtreme Gradient Boosting, Neural Network, and Support Vector Machine algorithms. In all cases, the spatial component is incorporated. The most influential variables are selected based on correlation and Lasso regularization techniques. It is determined that certain preexisting health conditions (comorbidities), the type of vaccine, age, and health insurance regime are found to be significantly associated with COVID-19 cases by neighborhood in the city of Cali.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias - Estadísticaspa
dc.format.extentx, 99 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84623
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadísticaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.decsCOVID-19spa
dc.subject.decsEnfermedad del Coronavirus-19eng
dc.subject.proposalCOVID-19spa
dc.subject.proposalCOVID-19eng
dc.subject.proposalSARS-CoV-2spa
dc.subject.proposalSARS-CoV-2eng
dc.subject.proposalEconometría espacialspa
dc.subject.proposalSpatial econometricseng
dc.subject.proposalModelos Lineales Generalizadosspa
dc.subject.proposalGeneralized Linear Modelseng
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalMachine learningeng
dc.subject.proposalAprendizaje profundospa
dc.subject.proposalDeep learningeng
dc.titleModelar la incidencia de la infección por COVID-19 en el área metropolitana de Santiago de Cali, en términos de variables socioeconómicas, demográficas y de salud, usando métodos estadísticos, de econometría espacial y machine learning, en el periodo comprendido de marzo 2020 - junio 2021spa
dc.title.translatedModel the incidence of COVID-19 infection in the metropolitan area of Santiago de Cali, in terms of socioeconomic, demographic and health variables, using statistical methods, spatial econometrics and machine learning, in the period from march 2020 - June 2021eng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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