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https://www.arca.fiocruz.br/handle/icict/65600
VISUALIZING FIT BETWEEN DENGUE AND CLIMATIC VARIABLES ON CAPITALS OF THE BRAZILIAN NORTHEAST REGION BY GENERALIZED ADDITIVE MODELS
Affilliation
Universidade do Estado do Rio de Janeiro. Programa de Pós-graduação em Meio Ambiente. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Centro de Ciências Matemáticas e da Natureza. Departamento de Meteorologia do Instituto de Geociências. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Laboratório de Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Centro de Ciências Matemáticas e da Natureza. Departamento de Meteorologia do Instituto de Geociências. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Laboratório de Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil.
Abstract
Recent analysis indicates that the numbers of dengue cases may be as high as 400 million/year in the world. According to the Ministry of Brazilian Health,
in 2015, there were 1,621,797 probable cases of dengue in the country including all classifications except discarded, the highest number recorded in the
historical series since 1990. Many studies have found associations between climatic conditions and dengue transmission, especially using generalized
models. In this study, Generalized Additive Models (GAM) was used associated to visreg package to understand the effect of climatic variables on capitals of Northeast Brazilian, from 2001 to 2012. From 12 climatic variables, it was verified that the relative humidity was the one that obtained the highest correlation to dengue. Afterwards, GAM associated with visreg was applied to understand the effects between them. Relative humidity explains the dengue incidence at an adjusted rate of 78.0% (in São Luis-MA) and 82.3% (in Teresina-PI) delayed in, respectively, −1 and −2 months.
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