Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos

Detalhes bibliográficos
Autor(a) principal: Bastos, Marcio Maciel
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: http://hdl.handle.net/10438/21991
Resumo: The Zika virus (ZIKV) is a pathogen of the family Flaviviridae, transmitted in Brazil mainly by the mosquito Aedes aegypti and in less extent by sexual relations. In addition to symptoms common to dengue and chikungunya, the zika virus is also capable of causing irreversible damage to the nervous system, in adults it is related to Guillain-Barr´e syndrome and in fetuses it causes microcephaly. The Health Department of Rio de Janeiro maintains a database with records of patients who sought care and was infeccted with Zika. Our study seeks to estimate the true size of the epidemic that occurred in the year 2016 and the parameters that fit to explain the dissemination process. To make these estimates, we used the data provided by the Health Department and a hierarchical Bayesian model adapted to the SIR epidemiological model. We perform the inference process through modern sampling techniques such as Automatic Differentiation Variational Inference (ADVI), Stein Variational Gradient Descent (SVGD) and No-U-Turn (NUTS).
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spelling Bastos, Marcio MacielEscolas::EMApSantos, Rodrigo Targino dosStruchiner, Claudio JoséCoelho, Flávio Codeço2018-04-12T18:21:12Z2018-04-12T18:21:12Z2018-03-06http://hdl.handle.net/10438/21991The Zika virus (ZIKV) is a pathogen of the family Flaviviridae, transmitted in Brazil mainly by the mosquito Aedes aegypti and in less extent by sexual relations. In addition to symptoms common to dengue and chikungunya, the zika virus is also capable of causing irreversible damage to the nervous system, in adults it is related to Guillain-Barr´e syndrome and in fetuses it causes microcephaly. The Health Department of Rio de Janeiro maintains a database with records of patients who sought care and was infeccted with Zika. Our study seeks to estimate the true size of the epidemic that occurred in the year 2016 and the parameters that fit to explain the dissemination process. To make these estimates, we used the data provided by the Health Department and a hierarchical Bayesian model adapted to the SIR epidemiological model. We perform the inference process through modern sampling techniques such as Automatic Differentiation Variational Inference (ADVI), Stein Variational Gradient Descent (SVGD) and No-U-Turn (NUTS).O Zika virus (ZIKV) é um patógeno da família Flaviviridae transmitido no Brasil principalmente pelo mosquito Aedes aegypti e em menor escala por relações sexuais. Além dos sintomas comuns à dengue e chikungunya, o vírus da zika também é capaz de causar danos irreversíveis no sistema nervoso, em adultos está relacionada à síndrome de Guillain-Barré e em fetos provoca microcefalia. O sistema de saúde do Rio de Janeiro mantém um banco de dados com os registros dos pacientes que buscaram atendimento e apresentaram sintomas de Zika. O nosso estudo busca estimar o verdadeiro tamanho da epidemia que ocorreu no ano de 2016 e os parâmetros que podem ser ajustados para explicar o processo de disseminação. Para realizar essas estimativas, utilizamos os dados fornecidos pelo sistema de saúde e uma modelagem Bayesiana hierárquica adaptada ao modelo epidemiológico SIR. 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dc.title.por.fl_str_mv Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
title Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
spellingShingle Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
Bastos, Marcio Maciel
Zika virus
Estimativa da epidemia de Zika
Matemática
Vírus da Zika
Modelagem de dados
Teoria bayesiana de decisão estatística
Epidemiologia - Modelos matemáticos
title_short Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
title_full Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
title_fullStr Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
title_full_unstemmed Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
title_sort Modelagem probabilística da dinâmica da Zika usando modelos hierárquicos bayesianos
author Bastos, Marcio Maciel
author_facet Bastos, Marcio Maciel
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EMAp
dc.contributor.member.none.fl_str_mv Santos, Rodrigo Targino dos
Struchiner, Claudio José
dc.contributor.author.fl_str_mv Bastos, Marcio Maciel
dc.contributor.advisor1.fl_str_mv Coelho, Flávio Codeço
contributor_str_mv Coelho, Flávio Codeço
dc.subject.por.fl_str_mv Zika virus
Estimativa da epidemia de Zika
topic Zika virus
Estimativa da epidemia de Zika
Matemática
Vírus da Zika
Modelagem de dados
Teoria bayesiana de decisão estatística
Epidemiologia - Modelos matemáticos
dc.subject.area.por.fl_str_mv Matemática
dc.subject.bibliodata.por.fl_str_mv Vírus da Zika
Modelagem de dados
Teoria bayesiana de decisão estatística
Epidemiologia - Modelos matemáticos
description The Zika virus (ZIKV) is a pathogen of the family Flaviviridae, transmitted in Brazil mainly by the mosquito Aedes aegypti and in less extent by sexual relations. In addition to symptoms common to dengue and chikungunya, the zika virus is also capable of causing irreversible damage to the nervous system, in adults it is related to Guillain-Barr´e syndrome and in fetuses it causes microcephaly. The Health Department of Rio de Janeiro maintains a database with records of patients who sought care and was infeccted with Zika. Our study seeks to estimate the true size of the epidemic that occurred in the year 2016 and the parameters that fit to explain the dissemination process. To make these estimates, we used the data provided by the Health Department and a hierarchical Bayesian model adapted to the SIR epidemiological model. We perform the inference process through modern sampling techniques such as Automatic Differentiation Variational Inference (ADVI), Stein Variational Gradient Descent (SVGD) and No-U-Turn (NUTS).
publishDate 2018
dc.date.accessioned.fl_str_mv 2018-04-12T18:21:12Z
dc.date.available.fl_str_mv 2018-04-12T18:21:12Z
dc.date.issued.fl_str_mv 2018-03-06
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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