Climate-driven statistical models as effective predictors of local dengue incidence in costa rica: a generalized additive model and random forest approach
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| Autores: | , , , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2019 |
| Descripción: | Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/39931 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/39931 |
| Palabra clave: | mosquito-borne diseases dengue climate variables Costa Rica generalized additive models random forests enfermedades de trasmisión vectorial variables climáticas modelos aditivos generalizados bosques aleatorios |