Submitted by Marcio Maciel Bastos (marciomacielbastos@hotmail.com) on 2018-04-02T15:09:10Z
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Previous issue date: 2018-03-06 / 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). / 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. Realizamos o processo de inferência através de modernas técnicas de amostragem, como Automatic Differentiation Variational Inference (ADVI), Stein Variational Gradient Descent (SVGD) e No-U-Turn (NUTS).
| Identifer | oai:union.ndltd.org:IBICT/oai:bibliotecadigital.fgv.br:10438/21991 |
| Date | 06 March 2018 |
| Creators | Bastos, Marcio Maciel |
| Contributors | Santos, Rodrigo Targino dos, Struchiner, Claudio José, Escolas::EMAp, Coelho, Flávio Codeço |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional do FGV, instname:Fundação Getulio Vargas, instacron:FGV |
| Rights | info:eu-repo/semantics/openAccess |
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