Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá
ilustraciones, gráficas, tablas
- Autores:
-
Castro Gil, César David
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83045
- Palabra clave:
- 510 - Matemáticas::515 - Análisis
Coronavirus Infections/mortality
Infecciones por Coronavirus/mortalidad
COVID-19
Exceso de ceros
Modelos cero inflado
Modelo de Hurdle
Conteo
Espacio-temporal
Hurdle mode
Zero inflated model
Count
Spatio-temporal
Modelo matemático
Análisis estadístico
Mathematical models
Statistical analysis
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá |
| dc.title.translated.eng.fl_str_mv |
Spatio-temporal model for the deaths due to COVID-19 in Cundinamarca department and Bogota city |
| title |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá |
| spellingShingle |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá 510 - Matemáticas::515 - Análisis Coronavirus Infections/mortality Infecciones por Coronavirus/mortalidad COVID-19 Exceso de ceros Modelos cero inflado Modelo de Hurdle Conteo Espacio-temporal Hurdle mode Zero inflated model Count Spatio-temporal Modelo matemático Análisis estadístico Mathematical models Statistical analysis |
| title_short |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá |
| title_full |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá |
| title_fullStr |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá |
| title_full_unstemmed |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá |
| title_sort |
Modelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y Bogotá |
| dc.creator.fl_str_mv |
Castro Gil, César David |
| dc.contributor.advisor.spa.fl_str_mv |
Melo Martínez, Oscar Orlando |
| dc.contributor.author.spa.fl_str_mv |
Castro Gil, César David |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::515 - Análisis |
| topic |
510 - Matemáticas::515 - Análisis Coronavirus Infections/mortality Infecciones por Coronavirus/mortalidad COVID-19 Exceso de ceros Modelos cero inflado Modelo de Hurdle Conteo Espacio-temporal Hurdle mode Zero inflated model Count Spatio-temporal Modelo matemático Análisis estadístico Mathematical models Statistical analysis |
| dc.subject.decs.eng.fl_str_mv |
Coronavirus Infections/mortality |
| dc.subject.decs.spa.fl_str_mv |
Infecciones por Coronavirus/mortalidad |
| dc.subject.proposal.spa.fl_str_mv |
COVID-19 Exceso de ceros Modelos cero inflado Modelo de Hurdle Conteo Espacio-temporal |
| dc.subject.proposal.eng.fl_str_mv |
Hurdle mode Zero inflated model Count Spatio-temporal |
| dc.subject.unesco.spa.fl_str_mv |
Modelo matemático Análisis estadístico |
| dc.subject.unesco.eng.fl_str_mv |
Mathematical models Statistical analysis |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-12-01 |
| dc.date.accessioned.none.fl_str_mv |
2023-01-20T16:55:47Z |
| dc.date.available.none.fl_str_mv |
2023-01-20T16:55:47Z |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/83045 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/83045 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
| dc.relation.references.spa.fl_str_mv |
Agarwal, D. K., Gelfand, A. & Citron-Pousty, S. (2002), ‘Zero-inflated models with appli- cation to spatial count data’, Environmental and Ecological Statistics 9, 341–345. Arab, A. (2015), ‘Spatial and spatio-temporal models for modeling epidemiological data with excess zeros’, International Journal of Environmental Research and Publich Health 12, 10536–10548. Besag, J., York, J. & Mollie, A. (1991), ‘Bayesian image restoration, with two applications in spatial statistics’, Annals of the Institute of Statistical Mathematics 43, 1–59. Blanco, L. (2004), Probabilidad, Universidad Nacional de Colombia. Blangiardo, M., Cameletti, M., Baio, G. & Rue, H. (2013), ‘Spatial and spatio-temporal models with R-INLA’, Spatial and Spatio-Temporal Epidemiology 4, 33–49. Byers, A., Allore, H., Gill, T. & Peduzzi, P. (2003), ‘Application of negative binomial modeling for discrete outcomes’, Journal of clinical epidemiology 56, 559–64. Fahrmeir, L. & Echavarría, L. O. (2006), ‘Structured additive regression for overdispersed and zero-inflated count data’, Applied Stochastic Models in Bussines and Industry 22, 351–369. Fuglstad, G.-A., Simpson, D., Lindgren, F. & Rue, H. (2018), ‘Constructing priors that penalize the complexity of gaussian random fields’, Journal of the American Statistical Association 114. Lambert, D. (1992), ‘Zero-inflated Poisson regression, with an application to defects in manufacturing’, Technometrics 34, 1–14. Mullahy, J. (1986), ‘Specification and testing of some modified count data models’, Journal of Econometrics 33, 341–366. Rodríguez, G. (2007), Lecture notes on generalized linear models, Technical report, Princeton University. Romo, J. E. (2019), Modelos espaciales y espacio-temporales para modelación de datos con exceso de ceros, Tesís de maestría, Centro de Investigación en Matemáticas (CIMAT). Rue, H. & Held, L. (2005), Gaussian Markov Random Fields, Theory and Applications, A Chapman & Hall Book. Rue, H. & Martino, S. (2007), ‘Approximate bayesian inference for hierarchical Gaussian Markov random fields’, Journal of Statistical Planning and Inference 137, 3177– 3192. Rue, H. & Martino, S. (2009), Implementing approximate bayesian inference using integrated nested laplace approximation: a manual for the inla program, Technical report, Department of Mathematical Sciences, NTNU (Norway). Rue, H., Martino, S. & Chopin, N. (2009), ‘Approximate bayesian inference for latent Gaussian models by using integrated nested Laplace approximations’, Journal of the Royal Society Series B 71, 319–392. Saavedra, P., Santana, A., Bello, L., Pachecho, J. M. & Sanjuán, E. (2021), ‘A bayesian spatio-temporal analysis of mortality rates in Spain: application to the covid-19 2020 outbreak’, Population Health Metrics 1, 19–27. Sartorius, B., Lawson, A. & Pullan, R. (2021), ‘Modelling and predicting the spatiotemporal spread of covid-19, associated deaths and impact of key risk factors in England’, Scientific Reports 11, 19–27. Spiegelhalter, D., Best, N., Carlin, B. & van der Linde, A. (2002), ‘Bayesian measures of model complexity and fit’, Journal of the Royal Society Series B 64, 583–639. Stroup, W. (2013), Generalized Linear Mixed Models,Modern Concepts, Methods and Applications, A Chapman & Hall Book. Torabi, M. (2017), ‘Zero-inflated spatio-temporal models for disease mapping’, Biometrical Journal 3, 430–434. Wikle, C. K. & Anderson, C. J. (2003), ‘Climatological analysis of tornado report counts using a hierarchical bayesian spatiotemporal model’, Journal of Geophysical Research 108, 9005. Zhu, S., Bukharin, A., Xie, L., Santillana, M., Yang, S. & Xie, Y. (2021), ‘High-resolution spatio-temporal model for county-level covid-19 activity in the U.S.’, Harvard University. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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vii, 62 páginas |
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application/pdf |
| dc.coverage.city.spa.fl_str_mv |
Bogotá |
| dc.coverage.country.spa.fl_str_mv |
Colombia |
| dc.coverage.region.spa.fl_str_mv |
Departamento de Cundinamarca |
| dc.coverage.tgn.none.fl_str_mv |
http://vocab.getty.edu/page/tgn/1000838 http://vocab.getty.edu/page/tgn/1000583 |
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Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias - Maestría en Ciencias - Estadística |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Melo Martínez, Oscar Orlando653518c1f1441d004f4edeffc2a59886Castro Gil, César Davidfb8c8e42466d3900cb02e6973a9e46752023-01-20T16:55:47Z2023-01-20T16:55:47Z2022-12-01https://repositorio.unal.edu.co/handle/unal/83045Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasEn el presente trabajo se ajusta un modelo espacio-temporal para el número de muertes a causa de COVID-19 en Cundinamarca y Bogotá. En los municipios más alejados de Cundinamarca lo normal es que no se presentarán muertes en mucho tiempo, por esta razón puede ser considerado un problema con exceso de ceros de espacio-tiempo. Además, se realizó un análisis exploratorio de la base de datos el cual permitió detectar la presencia de relación temporal y espacial. Por lo tanto, en una primera parte se exponen y detallan las metodologías y conceptos que pueden ayudar a manejar el exceso de ceros y posteriormente, se hace énfasis en el modelo espacio-temporal con exceso de ceros. En la búsqueda de la literatura se encontró que una buena alternativa para ajustar un modelo de este estilo es hacerlo mediante un modelo jerárquico Bayesiano usando el método de la aproximación de Laplace integrada anidada (INLA). Se realizó un análisis descriptivo de la vacunación en Colombia dejando algunos detalles que permitieron complementar el análisis del ajuste de los modelos. Finalmente, se obtuvo que el modelo que mejor se ajustó a la luz de la media del error absoluto de predicción (MAPE), el criterio de información de la devianza (DIC) y del contexto del exceso de ceros fue el modelo Poisson Cero Inflado. Asi, se puede afirmar que las muertes a causa de COVID-19 en Cundinamarca y Bogotá es un fenómeno espacio-temporal con exceso de ceros. (Texto tomado de la fuente).In the present work, a spatio-temporal model is fitted for the number of deaths due to COVID-19 in Cundinamarca department and Bogota city. In the most remote municipalities of Cundinamarca, it is normal that there will be no deaths in a long time, for this reason it can be considered a problem with of space-time excess of zeros. In addition, an exploratory analysis of the database was carried out, which allowed detecting the presence of a temporal and spatial relationship. Therefore, in the first part, the methodologies and concepts that can help to manage the excess of zeros are presented and detailed, and subsequently, emphasis is placed on the spatio-temporal model with excess of zeros. In the literature, it was found that the best alternative to fit a model of this style is usign a Bayesian hierarchical model Integrated Nested Laplace Approximation (INLA) method. A descriptive analysis of vaccination in Colombia was carried out, leaving some details that allowed complementing the analysis of model fitting. Finally, it was obtained that the best fitting model in light of the mean absolute prediction error (MAPE), the deviancy information criterion (DIC) and the context of the excess of zeros was the Zero Inflated Poisson model. Therefore, it can be affirmed that deaths due to COVID-19 in Cundinamarca and Bogotá is a spatio-temporal phenomenon with an excess of zeros.Incluye anexosMaestríaMagíster en Ciencias - Estadísticavii, 62 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::515 - AnálisisCoronavirus Infections/mortalityInfecciones por Coronavirus/mortalidadCOVID-19Exceso de cerosModelos cero infladoModelo de HurdleConteoEspacio-temporalHurdle modeZero inflated modelCountSpatio-temporalModelo matemáticoAnálisis estadísticoMathematical modelsStatistical analysisModelo espacio - temporal para las muertes a causa de COVID-19 en Cundinamarca y BogotáSpatio-temporal model for the deaths due to COVID-19 in Cundinamarca department and Bogota cityTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBogotáColombiaDepartamento de Cundinamarcahttp://vocab.getty.edu/page/tgn/1000838http://vocab.getty.edu/page/tgn/1000583Agarwal, D. K., Gelfand, A. & Citron-Pousty, S. (2002), ‘Zero-inflated models with appli- cation to spatial count data’, Environmental and Ecological Statistics 9, 341–345.Arab, A. (2015), ‘Spatial and spatio-temporal models for modeling epidemiological data with excess zeros’, International Journal of Environmental Research and Publich Health 12, 10536–10548.Besag, J., York, J. & Mollie, A. (1991), ‘Bayesian image restoration, with two applications in spatial statistics’, Annals of the Institute of Statistical Mathematics 43, 1–59.Blanco, L. (2004), Probabilidad, Universidad Nacional de Colombia.Blangiardo, M., Cameletti, M., Baio, G. & Rue, H. (2013), ‘Spatial and spatio-temporal models with R-INLA’, Spatial and Spatio-Temporal Epidemiology 4, 33–49.Byers, A., Allore, H., Gill, T. & Peduzzi, P. (2003), ‘Application of negative binomial modeling for discrete outcomes’, Journal of clinical epidemiology 56, 559–64.Fahrmeir, L. & Echavarría, L. O. (2006), ‘Structured additive regression for overdispersed and zero-inflated count data’, Applied Stochastic Models in Bussines and Industry 22, 351–369.Fuglstad, G.-A., Simpson, D., Lindgren, F. & Rue, H. (2018), ‘Constructing priors that penalize the complexity of gaussian random fields’, Journal of the American Statistical Association 114.Lambert, D. (1992), ‘Zero-inflated Poisson regression, with an application to defects in manufacturing’, Technometrics 34, 1–14.Mullahy, J. (1986), ‘Specification and testing of some modified count data models’, Journal of Econometrics 33, 341–366.Rodríguez, G. (2007), Lecture notes on generalized linear models, Technical report, Princeton University.Romo, J. E. (2019), Modelos espaciales y espacio-temporales para modelación de datos con exceso de ceros, Tesís de maestría, Centro de Investigación en Matemáticas (CIMAT).Rue, H. & Held, L. (2005), Gaussian Markov Random Fields, Theory and Applications, A Chapman & Hall Book.Rue, H. & Martino, S. (2007), ‘Approximate bayesian inference for hierarchical Gaussian Markov random fields’, Journal of Statistical Planning and Inference 137, 3177– 3192.Rue, H. & Martino, S. (2009), Implementing approximate bayesian inference using integrated nested laplace approximation: a manual for the inla program, Technical report, Department of Mathematical Sciences, NTNU (Norway).Rue, H., Martino, S. & Chopin, N. (2009), ‘Approximate bayesian inference for latent Gaussian models by using integrated nested Laplace approximations’, Journal of the Royal Society Series B 71, 319–392.Saavedra, P., Santana, A., Bello, L., Pachecho, J. M. & Sanjuán, E. (2021), ‘A bayesian spatio-temporal analysis of mortality rates in Spain: application to the covid-19 2020 outbreak’, Population Health Metrics 1, 19–27.Sartorius, B., Lawson, A. & Pullan, R. (2021), ‘Modelling and predicting the spatiotemporal spread of covid-19, associated deaths and impact of key risk factors in England’, Scientific Reports 11, 19–27.Spiegelhalter, D., Best, N., Carlin, B. & van der Linde, A. (2002), ‘Bayesian measures of model complexity and fit’, Journal of the Royal Society Series B 64, 583–639.Stroup, W. (2013), Generalized Linear Mixed Models,Modern Concepts, Methods and Applications, A Chapman & Hall Book.Torabi, M. (2017), ‘Zero-inflated spatio-temporal models for disease mapping’, Biometrical Journal 3, 430–434.Wikle, C. K. & Anderson, C. J. (2003), ‘Climatological analysis of tornado report counts using a hierarchical bayesian spatiotemporal model’, Journal of Geophysical Research 108, 9005.Zhu, S., Bukharin, A., Xie, L., Santillana, M., Yang, S. & Xie, Y. (2021), ‘High-resolution spatio-temporal model for county-level covid-19 activity in the U.S.’, Harvard University.AdministradoresBibliotecariosConsejerosEstudiantesGrupos comunitariosInvestigadoresMaestrosMedios de comunicaciónPadres y familiasPersonal de apoyo escolarProveedores de ayuda financiera para estudiantesPúblico generalReceptores de fondos federales y solicitantesResponsables políticosORIGINAL1072713162.2022.pdf1072713162.2022.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf6522565https://repositorio.unal.edu.co/bitstream/unal/83045/2/1072713162.2022.pdf9217f98367e775473012ba52d3f3cd34MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83045/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51THUMBNAIL1072713162.2022.pdf.jpg1072713162.2022.pdf.jpgGenerated 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