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
ilustraciones, diagramas, mapas
- Autores:
-
Saenz Perilla, Juan Pablo
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84623
- Palabra clave:
- COVID-19
Enfermedad del Coronavirus-19
COVID-19
SARS-CoV-2
Econometría espacial
Modelos Lineales Generalizados
Aprendizaje automático
Aprendizaje profundo
COVID-19
SARS-CoV-2
Spatial econometrics
Generalized Linear Models
Machine learning
Deep learning
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
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 |
| dc.title.translated.eng.fl_str_mv |
Model 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 2021 |
| title |
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 |
| spellingShingle |
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 COVID-19 Enfermedad del Coronavirus-19 COVID-19 SARS-CoV-2 Econometría espacial Modelos Lineales Generalizados Aprendizaje automático Aprendizaje profundo COVID-19 SARS-CoV-2 Spatial econometrics Generalized Linear Models Machine learning Deep learning |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
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 |
| dc.creator.fl_str_mv |
Saenz Perilla, Juan Pablo |
| dc.contributor.advisor.none.fl_str_mv |
Bohorquez Castañeda, Martha Patricia |
| dc.contributor.author.none.fl_str_mv |
Saenz Perilla, Juan Pablo |
| dc.subject.decs.spa.fl_str_mv |
COVID-19 |
| topic |
COVID-19 Enfermedad del Coronavirus-19 COVID-19 SARS-CoV-2 Econometría espacial Modelos Lineales Generalizados Aprendizaje automático Aprendizaje profundo COVID-19 SARS-CoV-2 Spatial econometrics Generalized Linear Models Machine learning Deep learning |
| dc.subject.decs.eng.fl_str_mv |
Enfermedad del Coronavirus-19 |
| dc.subject.proposal.spa.fl_str_mv |
COVID-19 SARS-CoV-2 Econometría espacial Modelos Lineales Generalizados Aprendizaje automático Aprendizaje profundo |
| dc.subject.proposal.eng.fl_str_mv |
COVID-19 SARS-CoV-2 Spatial econometrics Generalized Linear Models Machine learning Deep learning |
| description |
ilustraciones, diagramas, mapas |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-08-31T20:57:02Z |
| dc.date.available.none.fl_str_mv |
2023-08-31T20:57:02Z |
| dc.date.issued.none.fl_str_mv |
2023-02 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/84623 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/84623 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Agarwal, P. and Skupin, A. (2008). Self-organising maps : applications in geographic information science. Aggarwal, C. C. et al. (2018). Neural networks and deep learning. Springer, 10(978):3. Al-Hasani, G., Asaduzzaman, M., and Soliman, A.-H. (2021). Geographically weighted poisson regression models with different kernels: Application to road traffic accident data. Communications in Statistics: Case Studies, Data Analysis and Applications, 7(2):166–181. Anselin, L. and Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. Statistics textbooks and monographs, 155:237–290. Arbia, G. (2014). A primer for spatial econometrics with applications in r. Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., and Atkinson, P. M. (2020). Covid-19 outbreak prediction with machine learning. Algorithms, 13(10):249. Behrens, T., Schmidt, K., Viscarra Rossel, R. A., Gries, P., Scholten, T., and MacMillan, R. A. (2018). Spatial modelling with euclidean distance fields and machine learning. European journal of soil science, 69(5):757–770. Bohorquez, M. (2020). Estadística espacial y espacio-temporal para campos aleatorios escalares y funcionales. Borah, S. and Panigrahi, R. (2022). Applied soft computing: techniques and applications. Borcard, D. and Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological modelling, 153(1-2):51–68. Breiman, L. (1996). Bagging predictors. Machine learning, 24:123–140. Breiman, L. (2001). Random forests. Machine learning, 45:5–32. Brenning, A. (2012). Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The r package sperrorest. In 2012 IEEE international geoscience and remote sensing symposium, pages 5372–5375. IEEE. Brunsdon, C., Fotheringham, A. S., and Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4):281–298. Casella, G. and Berger, R. (2001). Statistical inference, 2nd edn. ser. Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data, pages 785–794. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20:273–297. Cristianini, N., Shawe-Taylor, J., et al. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press. Cuartas, D. E., Arango-Londoño, D., Guzmán-Escarria, G., Muñoz, E., Caicedo, D., Ortega, D., Fandiño-Losada, A., Mena, J., Torres, M., Barrera, L., et al. (2020). Análisis espacio-temporal del sars-cov-2 en cali, colombia. Revista de Salud Pública, 22(2):138–143. de Jong, P., Sprenger, C., and van Veen, F. (1984). On extreme values of moran’s i and geary’s c. Geographical Analysis, 16(1):17–24. Dobson, A. J. (2002). An introduction to generalized linear models. Dong, Z., Zhu, S., Xie, Y., Mateu, J., and Rodríguez-Cortés, F. J. (2021). Non-stationary spatio-temporal point process modeling for high-resolution covid-19 data. arXiv preprint arXiv:2109.09029. Dray, S., Legendre, P., and Peres-Neto, P. R. (2006). Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (pcnm). Ecological modelling, 196(3-4):483–493. Fotheringham, A. S., Brunsdon, C., and Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons. Frank, L. E. and Friedman, J. H. (1993). A statistical view of some chemometrics regression tools. Technometrics, 35(2):109–135. Gahegan, M. (2000). On the application of inductive machine learning tools to geographical analysis. Geographical analysis, 32(2):113–139. Gilardi, N. and Bengio, S. (2000). Local machine learning models for spatial data analysis. Journal of Geographic Information and Decision Analysis, 4(ARTICLE):11–28. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press. Gower, J. C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53(3-4):325–338. Griffith, D. A. and Griffith, D. A. (2003). Spatial filtering. Springer. Harris, R. (2020). Exploring the neighbourhood-level correlates of covid-19 deaths in london using a difference across spatial boundaries method. Health & place, 66:102446. Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer. Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., and Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18–28. Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., and Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6:e5518. Huang, C.-J., Shen, Y., Kuo, P.-H., and Chen, Y.-H. (2022). Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019. Socio-Economic Planning Sciences, 80:100976. Huber, P. J. (1992). Robust estimation of a location parameter. Breakthroughs in statistics: Methodology and distribution, pages 492–518. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 112. Springer. Kopczewska, K. (2022). Spatial machine learning: new opportunities for regional science. The Annals of Regional Science, 68(3):713–755. Le Rest, K., Pinaud, D., Monestiez, P., Chadoeuf, J., and Bretagnolle, V. (2014). Spatial leave-one-out cross validation for variable selection in the presence of spatial autocorrelation. Global ecology and biogeography, 23(7):811–820. Lee, C.-H., Greiner, R., and Schmidt, M. (2005). Support vector random fields for spatial classification. In Knowledge Discovery in Databases: PKDD 2005: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005. Proceedings 9, pages 121–132. Springer. Li, Z. and Sillanpää, M. J. (2012). Overview of lasso-related penalized regression methods for quantitative trait mapping and genomic selection. Theoretical and applied genetics, 125:419–435. Lindsey, J. K. (2000). Applying generalized linear models. Springer Science & Business Media. Lovelace, R., Nowosad, J., and Muenchow, J. (2019). Geocomputation with R. Chapman and Hall/CRC. Luo, Y., Yan, J., and McClure, S. (2021). Distribution of the environmental and socioeconomic risk factors on covid-19 death rate across continental usa: a spatial nonlinear analysis. Environmental Science and Pollution Research, 28:6587–6599. Maimon, O. and Rokach, L. (2005). Data mining and knowledge discovery handbook. Maiti, A., Zhang, Q., Sannigrahi, S., Pramanik, S., Chakraborti, S., Cerda, A., and Pilla, F. (2021). Exploring spatiotemporal effects of the driving factors on covid-19 incidences in the contiguous united states. Sustainable cities and society, 68:102784. Mateu, J. and Jalilian, A. (2022). Spatial point processes and neural networks: A convenient couple. Spatial Statistics, 50:100644. Mccullagh, P. and Nelder, J. A. (1989). Generalized linear models. Melin, P., Monica, J. C., Sanchez, D., and Castillo, O. (2020). Analysis of spatial spread relationships of coronavirus (covid-19) pandemic in the world using self organizing maps. Chaos, Solitons & Fractals, 138:109917. Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., and Nauss, T. (2018). Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101:1–9. Meyer, H., Reudenbach, C., Wöllauer, S., and Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications–moving from data reproduction to spatial prediction. Ecological Modelling, 411:108815. Middya, A. I. and Roy, S. (2022). Spatio-temporal variation of covid-19 health outcomes in india using deep learning based models. Technological Forecasting and Social Change, 183:121911. Mohebbi, M., Wolfe, R., and Jolley, D. (2011). A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations. BMC Medical Research Methodology, 11:1–11. Nelder, J. A. and Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3):370–384. Nikparvar, B., Rahman, M. M., Hatami, F., and Thill, J.-C. (2021). Spatio-temporal prediction of the covid-19 pandemic in us counties: modeling with a deep lstm neural network. Scientific reports, 11(1):21715. Nikparvar, B. and Thill, J.-C. (2021). Machine learning of spatial data. ISPRS International Journal of Geo-Information, 10(9):600. Niraula, P., Mateu, J., and Chaudhuri, S. (2022). A bayesian machine learning approach for spatio-temporal prediction of covid-19 cases. Stochastic Environmental Research and Risk Assessment, 36(8):2265–2283. Pohjankukka, J., Pahikkala, T., Nevalainen, P., and Heikkonen, J. (2017). Estimating the prediction performance of spatial models via spatial k-fold cross validation. International Journal of Geographical Information Science, 31(10):2001–2019. Pourghasemi, H. R., Pouyan, S., Heidari, B., Farajzadeh, Z., Shamsi, S. R. F., Babaei, S., Khosravi, R., Etemadi, M., Ghanbarian, G., Farhadi, A., et al. (2020). Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (covid-19) in iran (days between february 19 and june 14, 2020). International Journal of Infectious Diseases, 98:90–108. Quiñones, S., Goyal, A., and Ahmed, Z. U. (2021). Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (t2d) prevalence in the usa. Scientific reports, 11(1):6955. Reyes, P. M., Jaramillo, A. H., and Rojas, L. R. (2020). Efecto de factores socio-económicos y condiciones de salud en el contagio de covid-19 en los estados de México. Contaduría y administración, 65(5):17. Rogerson, P. A. and Fotheringham, S. (2009). The sage handbook of spatial analysis. Saefuddin, A., Saepudin, D., and Kusumaningrum, D. (2013). Geographically weighted poisson regression (gwpr) for analyzing the malnutrition data in java-indonesia. Sánchez A, V. D. (2003). Advanced support vector machines and kernel methods. Neurocomputing, 55(1-2):5–20. Schratz, P., Becker, M., Lang, M., and Brenning, A. (2021). mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in r. arXiv preprint arXiv:2110.12674. Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., and Brenning, A. (2018). Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data. arXiv preprint arXiv:1803.11266. Shafizadeh-Moghadam, H., Hagenauer, J., Farajzadeh, M., and Helbich, M. (2015). Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study. International Journal of Geographical Information Science, 29(4):606–623. Shao, Q., Xu, Y., Wu, H., et al. (2021). Spatial prediction of covid-19 in china based on machine learning algorithms and geographically weighted regression. Computational and Mathematical Methods in Medicine, 2021. Stojanova, D., Ceci, M., Appice, A., Malerba, D., and Džeroski, S. (2011). Global and local spatial autocorrelation in predictive clustering trees. In Discovery Science: 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings 14, pages 307–322. Springer. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288. Tobler, W. R. (1979). Cellular geography. Philosophy in geography, pages 379–386. Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media. Vapnik, V., Burges, C. J., and Schoelkopf, B. (1995). A new method for constructing artificial neural networks. Vapnik, V. and Vapnik, V. (1998). Statistical learning theory wiley. New York, 1(624):2. Wagner, M. and Zeileis, A. (2019). Heterogeneity and spatial dependence of regional growth in the EU: A recursive partitioning approach. German Economic Review, 20(1):67–82. Wang, L., Xu, T., Stoecker, T., Stoecker, H., Jiang, Y., and Zhou, K. (2021). Machine learning spatio-temporal epidemiological model to evaluate germany-county-level covid-19 risk. Machine Learning: Science and Technology, 2(3):035031. who (2020). Estimación de la mortalidad de la covid-19. Wu, C., Zhou, M., Liu, P., and Yang, M. (2021). Analyzing covid-19 using multisource data: An integrated approach of visualization, spatial regression, and machine learning. GeoHealth, 5(8):e2021GH000439. You, H., Wu, X., and Guo, X. (2020). Distribution of covid-19 morbidity rate in association with social and economic factors in wuhan, china: Implications for urban development. International journal of environmental research and public health, 17(10):3417. Zoabi, Y., Deri-Rozov, S., and Shomron, N. (2021). Machine learning-based prediction of covid-19 diagnosis based on symptoms. npj digital medicine, 4(1):3. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional |
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2021 - 2021 |
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Cali |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Estadística |
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Facultad de Ciencias |
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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_abf2Bohorquez Castañeda, Martha Patricia2e2e02de049d58b3081d25a3e7e00efdSaenz Perilla, Juan Pabloa7324af3b8ba2116df1df21c2f71ccf32021 - 2021Cali2023-08-31T20:57:02Z2023-08-31T20:57:02Z2023-02https://repositorio.unal.edu.co/handle/unal/84623Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapasEl 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)The 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.MaestríaMagíster en Ciencias - Estadísticax, 99 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede BogotáModelar 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 2021Model 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 2021Trabajo de grado - Maestríainfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAgarwal, P. and Skupin, A. (2008). Self-organising maps : applications in geographic information science.Aggarwal, C. C. et al. (2018). Neural networks and deep learning. Springer, 10(978):3.Al-Hasani, G., Asaduzzaman, M., and Soliman, A.-H. (2021). Geographically weighted poisson regression models with different kernels: Application to road traffic accident data. Communications in Statistics: Case Studies, Data Analysis and Applications, 7(2):166–181.Anselin, L. and Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. Statistics textbooks and monographs, 155:237–290.Arbia, G. (2014). A primer for spatial econometrics with applications in r.Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., and Atkinson, P. M. (2020). Covid-19 outbreak prediction with machine learning. Algorithms, 13(10):249.Behrens, T., Schmidt, K., Viscarra Rossel, R. A., Gries, P., Scholten, T., and MacMillan, R. A. (2018). 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Machine learning-based prediction of covid-19 diagnosis based on symptoms. npj digital medicine, 4(1):3.COVID-19Enfermedad del Coronavirus-19COVID-19SARS-CoV-2Econometría espacialModelos Lineales GeneralizadosAprendizaje automáticoAprendizaje profundoCOVID-19SARS-CoV-2Spatial econometricsGeneralized Linear ModelsMachine learningDeep learningLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84623/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1110583247.2023.pdf1110583247.2023.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf4479662https://repositorio.unal.edu.co/bitstream/unal/84623/2/1110583247.2023.pdf8faf5f0bb93e87a9b6385f2f9cd8948eMD52THUMBNAIL1110583247.2023.pdf.jpg1110583247.2023.pdf.jpgGenerated Thumbnailimage/jpeg4691https://repositorio.unal.edu.co/bitstream/unal/84623/3/1110583247.2023.pdf.jpgd87808248aff6c6c03f2d609eed5e076MD53unal/84623oai:repositorio.unal.edu.co:unal/846232024-08-13 23:38:27.343Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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