Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions

Detalhes bibliográficos
Autor(a) principal: Souza, Amaury de
Data de Publicação: 2020
Outros Autores: Santos, Débora Aparecida da Silva, Oliveira-Júnior, José Francisco de, Oliveira, Ana Paula Garcia, Silva, Elania Barros da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/6501
Resumo: Objective: To analyze the adjustments of the weibull, gamma, normal and logistic probability density distributions of the historical series of hospitalizations for respiratory diseases (childhood and adult pneumonia) from 2011 to 2015, in Campo Grande, MS. Methods: The shape and scale parameters of the distributions were determined to verify the quality of the data fit. Results: Four probability density functions (Table 2) were fitted and the R2, MAE, RSME, MAPE tests were used to verify the best density function for hospitalization data. Conclusion: The best fit was the Gamma distribution; the distribution can be used as an alternative distribution that adequately describes the data on hospital admissions for respiratory diseases in Campo Grande.
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spelling Modeling of hospital admissions for respiratory diseases as a function of probability distribution functionsModelado de ingresos hospitalarios por enfermedades respiratorias en función de las funciones de distribución de probabilidadModelagem de internações por doenças respiratórias em função das funções de distribuição de probabilidadeInternação hospitalarPneumoniaModelagemProbabilidadeCriança e adultos.Hospital admissionPneumoniaModelingProbabilityChild and adults.Ingreso hospitalarioNeumoníaModeladoProbabilidadNiños y adultos.Objective: To analyze the adjustments of the weibull, gamma, normal and logistic probability density distributions of the historical series of hospitalizations for respiratory diseases (childhood and adult pneumonia) from 2011 to 2015, in Campo Grande, MS. Methods: The shape and scale parameters of the distributions were determined to verify the quality of the data fit. Results: Four probability density functions (Table 2) were fitted and the R2, MAE, RSME, MAPE tests were used to verify the best density function for hospitalization data. Conclusion: The best fit was the Gamma distribution; the distribution can be used as an alternative distribution that adequately describes the data on hospital admissions for respiratory diseases in Campo Grande.Objetivo: analizar los ajustes de las distribuciones de densidad de probabilidad weibull, gamma, normal y logística de la serie histórica de hospitalizaciones por enfermedades respiratorias (neumonía infantil y adulta) de 2011 a 2015, en Campo Grande, MS. Métodos: se determinaron los parámetros de forma y escala de las distribuciones para verificar la calidad del ajuste de los datos. Resultados: se ajustaron cuatro funciones de densidad de probabilidad (Tabla 2) y se utilizaron las pruebas R2, MAE, RSME, MAPE para verificar la mejor función de densidad para los datos de hospitalización. Conclusión: el mejor ajuste fue la distribución Gamma; La distribución se puede utilizar como una distribución alternativa que describa adecuadamente los datos sobre ingresos hospitalarios por enfermedades respiratorias en Campo Grande.Objetivo: Analisar os ajustes das distribuições de densidade de probabilidade weibull, gama, normal e logística da série histórica de hospitalizações por doenças respiratórias (pneumonia infantil e adulto) de 2011 a 2015, em Campo Grande, MS. Métodos: Os parâmetros de forma e escala das distribuições foram determinados para verificar a qualidade do ajuste dos dados. Resultados: Quatro funções de densidade de probabilidade (Tabela 2) foram ajustadas e os testes R2, MAE, RSME, MAPE foram utilizados para verificar a melhor função de densidade para dados de hospitalização. Conclusão: O melhor ajuste foi a distribuição gama; a distribuição pode ser usada como uma distribuição alternativa que descreve adequadamente os dados de internações por doenças respiratórias em Campo Grande.Research, Society and Development2020-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/650110.33448/rsd-v9i8.6501Research, Society and Development; Vol. 9 No. 8; e869986501Research, Society and Development; Vol. 9 Núm. 8; e869986501Research, Society and Development; v. 9 n. 8; e8699865012525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/6501/5960Copyright (c) 2020 Amaury Souza, Débora Aparecida da Silva Santos, José Francisco de Oliveira-Júnior, Ana Paula Garcia Oliveira, Elania Barros da Silvahttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSouza, Amaury deSantos, Débora Aparecida da SilvaOliveira-Júnior, José Francisco deOliveira, Ana Paula GarciaSilva, Elania Barros da2020-08-20T18:00:17Zoai:ojs.pkp.sfu.ca:article/6501Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:29:36.638897Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
Modelado de ingresos hospitalarios por enfermedades respiratorias en función de las funciones de distribución de probabilidad
Modelagem de internações por doenças respiratórias em função das funções de distribuição de probabilidade
title Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
spellingShingle Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
Souza, Amaury de
Internação hospitalar
Pneumonia
Modelagem
Probabilidade
Criança e adultos.
Hospital admission
Pneumonia
Modeling
Probability
Child and adults.
Ingreso hospitalario
Neumonía
Modelado
Probabilidad
Niños y adultos.
title_short Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
title_full Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
title_fullStr Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
title_full_unstemmed Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
title_sort Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions
author Souza, Amaury de
author_facet Souza, Amaury de
Santos, Débora Aparecida da Silva
Oliveira-Júnior, José Francisco de
Oliveira, Ana Paula Garcia
Silva, Elania Barros da
author_role author
author2 Santos, Débora Aparecida da Silva
Oliveira-Júnior, José Francisco de
Oliveira, Ana Paula Garcia
Silva, Elania Barros da
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Souza, Amaury de
Santos, Débora Aparecida da Silva
Oliveira-Júnior, José Francisco de
Oliveira, Ana Paula Garcia
Silva, Elania Barros da
dc.subject.por.fl_str_mv Internação hospitalar
Pneumonia
Modelagem
Probabilidade
Criança e adultos.
Hospital admission
Pneumonia
Modeling
Probability
Child and adults.
Ingreso hospitalario
Neumonía
Modelado
Probabilidad
Niños y adultos.
topic Internação hospitalar
Pneumonia
Modelagem
Probabilidade
Criança e adultos.
Hospital admission
Pneumonia
Modeling
Probability
Child and adults.
Ingreso hospitalario
Neumonía
Modelado
Probabilidad
Niños y adultos.
description Objective: To analyze the adjustments of the weibull, gamma, normal and logistic probability density distributions of the historical series of hospitalizations for respiratory diseases (childhood and adult pneumonia) from 2011 to 2015, in Campo Grande, MS. Methods: The shape and scale parameters of the distributions were determined to verify the quality of the data fit. Results: Four probability density functions (Table 2) were fitted and the R2, MAE, RSME, MAPE tests were used to verify the best density function for hospitalization data. Conclusion: The best fit was the Gamma distribution; the distribution can be used as an alternative distribution that adequately describes the data on hospital admissions for respiratory diseases in Campo Grande.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/6501
10.33448/rsd-v9i8.6501
url https://rsdjournal.org/index.php/rsd/article/view/6501
identifier_str_mv 10.33448/rsd-v9i8.6501
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/6501/5960
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 9 No. 8; e869986501
Research, Society and Development; Vol. 9 Núm. 8; e869986501
Research, Society and Development; v. 9 n. 8; e869986501
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
_version_ 1797052738390982656

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