Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais

Detalhes bibliográficos
Ano de defesa: 2014
Autor(a) principal: Silva, Caroline Pafiadache da lattes
Orientador(a): Souza, Adriano Mendonça lattes
Banca de defesa: Bisognin, Cleber, Zanini, Roselaine Ruviaro
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
Centro de Tecnologia
Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção
Departamento: Engenharia de Produção
País: Brasil
Palavras-chave em Português:
Vigilância epidemiológica
Séries temporais
Modelo ARIMA
Modelo ARMAX
Modelo VAR
VAR model
Palavras-chave em Inglês:
Epidemiological surveillance
Time series
ARIMA model
ARIMAX model
Área do conhecimento CNPq:
CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
Link de acesso: http://repositorio.ufsm.br/handle/1/22046
Resumo: The analysis of time series obtained in the databases of public health plays an important role in processes of health surveillance. However, implementation of methodologies for time series has not yet become a routine in the midst of healthcare practitioners. The objective of this study is to present a theoretical review about time series analysis used for epidemiological surveillance data and practical application of statistical methods for the estimation of three models for notifiable diseases: the Box and Jenkins methodological in the presence and absence of exogenous variable (ARIMAX and ARIMA) and vector autoregression (VAR) model. For this, we perfomed a cross-sectional study using secondary data from SINAN (Information System for Notifiable Diseases) consisting of cases of hepatitis A and leptospirosis recorded in Rio Grande do Sul, in the period January 2008 to December 2012. The models were analyzed and discussed through comparison of performance measures. The ARIMA models presented the best properties for the prediction of new cases of the diseases studied. The one-way causality between the diseases was also established.
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spelling 2021-08-24T17:40:22Z2021-08-24T17:40:22Z2014-03-07http://repositorio.ufsm.br/handle/1/22046The analysis of time series obtained in the databases of public health plays an important role in processes of health surveillance. However, implementation of methodologies for time series has not yet become a routine in the midst of healthcare practitioners. The objective of this study is to present a theoretical review about time series analysis used for epidemiological surveillance data and practical application of statistical methods for the estimation of three models for notifiable diseases: the Box and Jenkins methodological in the presence and absence of exogenous variable (ARIMAX and ARIMA) and vector autoregression (VAR) model. For this, we perfomed a cross-sectional study using secondary data from SINAN (Information System for Notifiable Diseases) consisting of cases of hepatitis A and leptospirosis recorded in Rio Grande do Sul, in the period January 2008 to December 2012. The models were analyzed and discussed through comparison of performance measures. The ARIMA models presented the best properties for the prediction of new cases of the diseases studied. The one-way causality between the diseases was also established.A análise de séries históricas, obtidas nas bases de dados de saúde pública, desempenha um papel importante em processos de vigilância epidemiológica. No entanto, a implementação de metodologias de séries temporais ainda não se tornou uma rotina em meio aos profissionais dessa área. Neste trabalho são apresentados um survey da literatura sobre a análise de séries temporais empregada aos dados de vigilância epidemiológica e a aplicação prática de métodos estatísticos para a estimação de três modelos para doenças de notificação compulsória: modelagem de Box e Jenkins na presença e ausência da variável exógena ( e ) e modelo de vetor autorregressivo (). Para isso, foi realizado um estudo transversal com dados secundários provenientes do SINAN (Sistema de Informação de Agravos de Notificação) constituído pelos casos de Hepatite A e Leptospirose, registrados no Rio Grande do Sul, no período de janeiro de 2008 a dezembro de 2012. Os modelos foram analisados e discutidos comparativamente por meio de medidas de desempenho. Os modelos apresentaram as melhores propriedades para a previsão de novos casos dos agravos estudados. A relação de causalidade unidirecional entre as doenças também foi estabelecida.porUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia de ProduçãoUFSMBrasilEngenharia de ProduçãoAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessVigilância epidemiológicaSéries temporaisModelo ARIMAModelo ARMAXModelo VARVAR modelEpidemiological surveillanceTime seriesARIMA modelARIMAX modelCNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAOAnálise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporaisData analysis of surveillance through different kinds of time series modelsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisSouza, Adriano Mendonçahttp://lattes.cnpq.br/5271075797851198Bisognin, CleberZanini, Roselaine Ruviarohttp://lattes.cnpq.br/3786701151819919Silva, Caroline Pafiadache da300800000005600600600c8e52001-817e-46c0-b541-46e1da5d410e41307455-46b2-4527-b10a-f8fa50629b752fa2faf8-e1ad-45c8-95ec-3acedc3a1920abcfcf97-7580-466a-bc5f-de161fd3a14breponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEP_2014_SILVA_CAROLINE.pdfDIS_PPGEP_2014_SILVA_CAROLINE.pdfDissertação de Mestradoapplication/pdf823515http://repositorio.ufsm.br/bitstream/1/22046/1/DIS_PPGEP_2014_SILVA_CAROLINE.pdfbc6850d91700ea99bf4824456ae2d45dMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
dc.title.alternative.eng.fl_str_mv Data analysis of surveillance through different kinds of time series models
title Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
spellingShingle Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
Silva, Caroline Pafiadache da
Vigilância epidemiológica
Séries temporais
Modelo ARIMA
Modelo ARMAX
Modelo VAR
VAR model
Epidemiological surveillance
Time series
ARIMA model
ARIMAX model
CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
title_short Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
title_full Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
title_fullStr Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
title_full_unstemmed Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
title_sort Análise de dados de vigilância epidemiológica por meio de diferentes tipos de modelos de séries temporais
author Silva, Caroline Pafiadache da
author_facet Silva, Caroline Pafiadache da
author_role author
dc.contributor.advisor1.fl_str_mv Souza, Adriano Mendonça
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/5271075797851198
dc.contributor.referee1.fl_str_mv Bisognin, Cleber
dc.contributor.referee2.fl_str_mv Zanini, Roselaine Ruviaro
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/3786701151819919
dc.contributor.author.fl_str_mv Silva, Caroline Pafiadache da
contributor_str_mv Souza, Adriano Mendonça
Bisognin, Cleber
Zanini, Roselaine Ruviaro
dc.subject.por.fl_str_mv Vigilância epidemiológica
Séries temporais
Modelo ARIMA
Modelo ARMAX
Modelo VAR
VAR model
topic Vigilância epidemiológica
Séries temporais
Modelo ARIMA
Modelo ARMAX
Modelo VAR
VAR model
Epidemiological surveillance
Time series
ARIMA model
ARIMAX model
CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
dc.subject.eng.fl_str_mv Epidemiological surveillance
Time series
ARIMA model
ARIMAX model
dc.subject.cnpq.fl_str_mv CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
description The analysis of time series obtained in the databases of public health plays an important role in processes of health surveillance. However, implementation of methodologies for time series has not yet become a routine in the midst of healthcare practitioners. The objective of this study is to present a theoretical review about time series analysis used for epidemiological surveillance data and practical application of statistical methods for the estimation of three models for notifiable diseases: the Box and Jenkins methodological in the presence and absence of exogenous variable (ARIMAX and ARIMA) and vector autoregression (VAR) model. For this, we perfomed a cross-sectional study using secondary data from SINAN (Information System for Notifiable Diseases) consisting of cases of hepatitis A and leptospirosis recorded in Rio Grande do Sul, in the period January 2008 to December 2012. The models were analyzed and discussed through comparison of performance measures. The ARIMA models presented the best properties for the prediction of new cases of the diseases studied. The one-way causality between the diseases was also established.
publishDate 2014
dc.date.issued.fl_str_mv 2014-03-07
dc.date.accessioned.fl_str_mv 2021-08-24T17:40:22Z
dc.date.available.fl_str_mv 2021-08-24T17:40:22Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.ufsm.br/handle/1/22046
url http://repositorio.ufsm.br/handle/1/22046
dc.language.iso.fl_str_mv por
language por
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dc.relation.confidence.fl_str_mv 600
600
600
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dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
Centro de Tecnologia
dc.publisher.program.fl_str_mv Programa de Pós-Graduação em Engenharia de Produção
dc.publisher.initials.fl_str_mv UFSM
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv Engenharia de Produção
publisher.none.fl_str_mv Universidade Federal de Santa Maria
Centro de Tecnologia
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reponame_str Biblioteca Digital de Teses e Dissertações do UFSM
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