Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning

The purpose of this study is to build an epidemic model with vaccination control for Covid-19 in El Salvador. A combination of epidemiological SEIR (Susceptible, Exposed, Infectious or Recovered) models and the estimation of parameters using machine learning and contact networks is proposed. The pro...

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Detalles Bibliográficos
Autor: López-Sandoval, Víctor Edgardo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:Costa Rica
Institución:Universidad Nacional de Costa Rica
Repositorio:Portal de Revistas UNA
Idioma:español
OAI Identifier:oai:www.revistas.una.ac.cr:article/15216
Acceso en línea:https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216
Access Level:acceso abierto
Palabra clave:SEIR
Machine Learning
Epidemic Model
Vaccination
Covid-19
El Salvador
Modelo Epidemiológico
Vacunación
machine learning
modelo epidemiológico
vacinação
id CR_2fc1183da1d0cb1cd92929db5674ae37
oai_identifier_str oai:www.revistas.una.ac.cr:article/15216
network_acronym_str CR
network_name_str Costa Rica
repository_id_str
dc.title.none.fl_str_mv Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
Distribución de vacuna Covid-19: Combinando SEIR y Machine Learning
Distribuição de vacinas Covid-19: Combinando SEIR e Machine Learning
title Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
spellingShingle Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
López-Sandoval, Víctor Edgardo
SEIR
Machine Learning
Epidemic Model
Vaccination
Covid-19
El Salvador
SEIR
Machine Learning
Modelo Epidemiológico
Vacunación
Covid-19
El Salvador
SEIR
machine learning
modelo epidemiológico
vacinação
Covid-19
El Salvador
title_short Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
title_full Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
title_fullStr Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
title_full_unstemmed Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
title_sort Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning
dc.creator.none.fl_str_mv López-Sandoval, Víctor Edgardo
author López-Sandoval, Víctor Edgardo
author_facet López-Sandoval, Víctor Edgardo
author_role author
dc.subject.none.fl_str_mv SEIR
Machine Learning
Epidemic Model
Vaccination
Covid-19
El Salvador
SEIR
Machine Learning
Modelo Epidemiológico
Vacunación
Covid-19
El Salvador
SEIR
machine learning
modelo epidemiológico
vacinação
Covid-19
El Salvador
topic SEIR
Machine Learning
Epidemic Model
Vaccination
Covid-19
El Salvador
SEIR
Machine Learning
Modelo Epidemiológico
Vacunación
Covid-19
El Salvador
SEIR
machine learning
modelo epidemiológico
vacinação
Covid-19
El Salvador
description The purpose of this study is to build an epidemic model with vaccination control for Covid-19 in El Salvador. A combination of epidemiological SEIR (Susceptible, Exposed, Infectious or Recovered) models and the estimation of parameters using machine learning and contact networks is proposed. The project consisted of three phases: a) Analysis: the critical or key factors or variables of the phenomenon under study were identified, the model to be used, as well as its parameters and components, were defined, designed, and constructed b) Simulation: simulation made it possible to modify variables, implement alternatives, and modify the model itself without affecting the real system, which is highly useful for decision-making and preparing results and recommendations. The simulations were carried out using population data from El Salvador. c) Optimization: different scenarios were evaluated in which vaccination control measures and social distancing measures were applied, in order to identify the optimal strategy. As a result of this study, the best strategy for controlling the disease was identified: a combination of vaccinating the vulnerable population and maintaining social distancing measures provided the best results in terms of reducing the impact of infection and minimizing treatment costs. Finally, recommendations are made to government health authorities for distribution and application of the treatment.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-31
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
Papers evaluated by academic peers
Artículos evaluados por pares académicos
artículo original
http://purl.org/coar/resource_type/c_2df8fbb1
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216
10.15359/ru.36-1.12
url https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216
identifier_str_mv 10.15359/ru.36-1.12
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216/24040
https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216/24708
https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216/24709
dc.rights.none.fl_str_mv Derechos de autor 2022 compartidos: Revista y Autores(as) (CC-BY-NC-ND)
acceso abierto
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Derechos de autor 2022 compartidos: Revista y Autores(as) (CC-BY-NC-ND)
acceso abierto
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/html
application/epub+zip
dc.publisher.none.fl_str_mv Universidad Nacional, Costa Rica
publisher.none.fl_str_mv Universidad Nacional, Costa Rica
dc.source.none.fl_str_mv Uniciencia; Vol. 36 No. 1 (2022): Uniciencia. January-December, 2022; 1-15
Uniciencia; Vol. 36 Núm. 1 (2022): Uniciencia. January-December, 2022; 1-15
Uniciencia; v. 36 n. 1 (2022): Uniciencia. January-December, 2022; 1-15
2215-3470
reponame:Portal de Revistas UNA
instname:Universidad Nacional de Costa Rica
instacron:UNA
instname_str Universidad Nacional de Costa Rica
instacron_str UNA
institution UNA
reponame_str Portal de Revistas UNA
collection Portal de Revistas UNA
repository.name.fl_str_mv Portal de Revistas UNA - Universidad Nacional de Costa Rica
repository.mail.fl_str_mv andrea.mora.campos@una.cr
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spelling Covid-19 Vaccine Distribution: Combining SEIR and Machine LearningDistribución de vacuna Covid-19: Combinando SEIR y Machine LearningDistribuição de vacinas Covid-19: Combinando SEIR e Machine LearningLópez-Sandoval, Víctor EdgardoSEIRMachine LearningEpidemic ModelVaccinationCovid-19El SalvadorSEIRMachine LearningModelo EpidemiológicoVacunaciónCovid-19El SalvadorSEIRmachine learningmodelo epidemiológicovacinaçãoCovid-19El SalvadorThe purpose of this study is to build an epidemic model with vaccination control for Covid-19 in El Salvador. A combination of epidemiological SEIR (Susceptible, Exposed, Infectious or Recovered) models and the estimation of parameters using machine learning and contact networks is proposed. The project consisted of three phases: a) Analysis: the critical or key factors or variables of the phenomenon under study were identified, the model to be used, as well as its parameters and components, were defined, designed, and constructed b) Simulation: simulation made it possible to modify variables, implement alternatives, and modify the model itself without affecting the real system, which is highly useful for decision-making and preparing results and recommendations. The simulations were carried out using population data from El Salvador. c) Optimization: different scenarios were evaluated in which vaccination control measures and social distancing measures were applied, in order to identify the optimal strategy. As a result of this study, the best strategy for controlling the disease was identified: a combination of vaccinating the vulnerable population and maintaining social distancing measures provided the best results in terms of reducing the impact of infection and minimizing treatment costs. Finally, recommendations are made to government health authorities for distribution and application of the treatment.Este estudio tiene como objetivo general construir un modelo epidémico con control por vacunación para el Covid-19 en El Salvador. Se propone la combinación de modelos epidemiológicos SEIR (Susceptibles, Expuestos, Infectados o Recuperados) y la estimación de parámetros usando machine learning y redes de contacto. El proyecto se desarrolló siguiendo tres fases: a) Análisis: se realizó la identificación de factores o variables críticas o claves del fenómeno en estudio, se definió, diseñó y construyó el modelo a utilizar junto con sus parámetros y componentes. b) Simulación: una vez construido el modelo, se desarrolla una simulación de este. La simulación permitió modificar variables, implementar alternativas y hacer modificaciones al modelo sin afectar al sistema real, lo cual es de gran utilidad en la toma de decisiones y elaboración de resultados y recomendaciones. Se desarrollan las simulaciones con datos poblacionales de El Salvador. c) Optimización: se evaluaron diferentes escenarios en los cuales se aplican medidas de control por vacunación y medidas de distanciamiento social, con el objetivo de identificar la estrategia óptima. Como resultado del estudio se identificó como mejor estrategia para el control de la enfermedad: vacunar a la población vulnerable y mantener medidas de distanciamiento social, la combinación de estas dos políticas brindó los mejores resultados en función de disminuir el impacto de la infección y de minimizar los costos del tratamiento. Al final, se brindan recomendaciones a las autoridades de salud gubernamentales para la distribución y aplicación del tratamiento.Este estudo tem como objetivo geral construir um modelo epidêmico com controle por vacinação para a Covid-19 em El Salvador. Propõe-se a combinação de modelos epidemiológicos SEIR (Suscetíveis, Expostos, Infectados ou Recuperados) e a estimativa de parâmetros utilizando machine learning e redes de contato. O projeto foi desenvolvido a partir de três fases: a) Análise: foi realizada a identificação de fatores ou variáveis críticas ou chave do fenômeno em estudo, o modelo a ser utilizado foi definido, desenhado e construído juntamente com seus parâmetros e componentes. b) Simulação: uma vez que o modelo é construído, uma simulação dele é desenvolvida. A simulação permitiu modificar variáveis, implementar alternativas e fazer modificações no modelo sem afetar o sistema real, o que é muito útil na tomada de decisão e na elaboração de resultados e recomendações. Simulações são desenvolvidas com dados populacionais de El Salvador. c) Otimização: foram avaliados diferentes cenários em que são aplicadas medidas de controle de vacinação e medidas de distanciamento social, com o objetivo de identificar a estratégia ideal. Como resultado do estudo foi identificada como a melhor estratégia para o controle da doença: vacinar a população vulnerável e manter medidas de distanciamento social, a combinação dessas duas políticas proporcionou os melhores resultados em termos de redução de impacto da infecção e minimização dos custos do tratamento. No final, são fornecidas recomendações às autoridades governamentais de saúde para a distribuição e a aplicação do tratamento.Universidad Nacional, Costa Rica2022-01-31info:eu-repo/semantics/publishedVersionPapers evaluated by academic peersArtículos evaluados por pares académicosartículo originalhttp://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleapplication/pdftext/htmlapplication/epub+ziphttps://www.revistas.una.ac.cr/index.php/uniciencia/article/view/1521610.15359/ru.36-1.12Uniciencia; Vol. 36 No. 1 (2022): Uniciencia. January-December, 2022; 1-15Uniciencia; Vol. 36 Núm. 1 (2022): Uniciencia. January-December, 2022; 1-15Uniciencia; v. 36 n. 1 (2022): Uniciencia. January-December, 2022; 1-152215-3470reponame:Portal de Revistas UNAinstname:Universidad Nacional de Costa Ricainstacron:UNAspahttps://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216/24040https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216/24708https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/15216/24709Derechos de autor 2022 compartidos: Revista y Autores(as) (CC-BY-NC-ND)acceso abiertohttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccess2022-06-14T15:37:27Zoai:www.revistas.una.ac.cr:article/15216Portal de revistashttp://revistas.una.ac.cr/Universidadhttp://www.una.ac.crhttps://revistas.una.ac.cr/index.php/index/oaiandrea.mora.campos@una.crCosta RicaNo aplicaNo aplicaNo aplicaopendoar:2022-06-14T15:37:27Portal de Revistas UNA - Universidad Nacional de Costa Ricafalse
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