Climate-Driven Statistical Models as Effective Predictors of Local Dengue Incidence in Costa Rica: A Generalized Additive Model and Random Forest Approach

 

Guardado en:
Detalles Bibliográficos
Autores: Vásquez Brenes, Paola Andrea, Loría García, Antonio, Sánchez Peña, Fabio Ariel, Barboza Chinchilla, Luis Alberto
Formato: artículo original
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 CostaRica, 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 ap-plying 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:Kérwá
Institución:Universidad de Costa Rica
Repositorio:Kérwá
Lenguaje:Inglés
OAI Identifier:oai:https://www.kerwa.ucr.ac.cr:10669/83429
Acceso en línea:https://revistas.ucr.ac.cr/index.php/matematica/article/view/39931
https://hdl.handle.net/10669/83429
Access Level:acceso abierto
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