skip to main content
Visitante
Meu Espaço
Minha Conta
Sair
Identificação
This feature requires javascript
Tags
Revistas Eletrônicas (eJournals)
Livros Eletrônicos (eBooks)
Bases de Dados
Bibliotecas USP
Ajuda
Ajuda
Idioma:
Inglês
Espanhol
Português
This feature required javascript
This feature requires javascript
Primo Search
Busca Geral
Busca Geral
Acervo Físico
Acervo Físico
Produção Intelectual da USP
Produção USP
Search For:
Clear Search Box
Search in:
Produção Intelectual da USP
Or hit Enter to replace search target
Or select another collection:
Search in:
Produção Intelectual da USP
Busca Avançada
Busca por Índices
This feature requires javascript
This feature requires javascript
Data science for epidemiology: a case study of dengue in Brazil.
Roster, Kirstin Ingrid Oliveira
Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação 2022-12-19
Acesso online
Exibir Online
Detalhes
Resenhas & Tags
Mais Opções
This feature requires javascript
Enviar para
Adicionar ao Meu Espaço
Remover do Meu Espaço
E-mail (máximo 30 registros por vez)
Imprimir
Link permanente
Referência
EasyBib
EndNote
RefWorks
del.icio.us
Exportar RIS
Exportar BibTeX
This feature requires javascript
Título:
Data science for epidemiology: a case study of dengue in Brazil.
Autor:
Roster, Kirstin Ingrid Oliveira
Orientador:
Rodrigues, Francisco Aparecido
Assuntos:
Aprendizado De Máquina
;
Dengue
;
Inferência Causal
;
Previsão De Doenças
;
Causal Inference
;
Disease Forecasting
;
Machine Learning
Notas:
Tese (Doutorado)
Descrição:
This thesis is a collection of studies on the application of data science to problems in dengue epidemiology. We leverage machine learning models together with methods from causal inference for two important public health objectives: (i) forecasting disease prevalence to anticipate outbreaks and allocate resources, and (ii) understanding disease drivers to develop effective interventions. Using diverse data on disease prevalence, climate, and human behavior, we demonstrate how machine learning can be applied in three different contexts: first, to develop accurate predictions of infections across Brazilian cities; second, to generalize predictions to new diseases; and finally, as an intermediate step for causal inference. In Chapter 2, we compare machine learning algorithms for dengue prediction and assess the value of causal feature selection. We find variation in the optimal predictors in national (domain-invariant) and single-city (domain-specific) settings. Decision tree ensemble models perform best at national scale. Causal feature selection performs best according to one of four error metrics, though it is not the optimal method across all cities in single-city forecasts. This result helps us better understand the potential within-domain cost in predictive performance of causally-informed models. In Chapter 3, we assess the generalizability of the dengue models developed in the prior chapter. Based on the hypothesis that diseases may share common time series characteristics, we test the effectiveness of knowledge transfer from endemic to novel diseases to improve predictions in low-data settings. We compare instance- and parameter-based transfer learning algorithms and evaluate performance on both synthetic and empirical data. Results suggest that transfer learning offers potential for early pandemic response and that the most predictive algorithm and transfer method depends on the similarity of the disease pairs. In Chapter 4, we consider the contribution of machine learning to causal inference, by examining the impact of the COVID-19 pandemic on dengue in Brazil. We estimate the gap between expected and observed dengue cases using an interrupted time series design. We also decompose the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. This analysis helps shed light on the data gaps caused by the COVID-19 pandemic and more generally, on possible intervention targets to help control dengue in the future.
DOI:
10.11606/T.55.2022.tde-27022023-142607
Editor:
Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação
Data de criação/publicação:
2022-12-19
Formato:
Adobe PDF
Idioma:
Inglês
Links
Este item no Dedalus
Teses e Dissertações USP
Acesso ao doi
E-mail do orientador
This feature requires javascript
This feature requires javascript
Voltar para lista de resultados
This feature requires javascript
This feature requires javascript
Buscando em bases de dados remotas. Favor aguardar.
Buscando por
em
scope:(USP_PRODUCAO)
Mostrar o que foi encontrado até o momento
This feature requires javascript
This feature requires javascript