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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Bibliografische gegevens
Auteurs: Vásquez Brenes, Paola Andrea, Loría García, Antonio, Sánchez Peña, Fabio Ariel, Barboza Chinchilla, Luis Alberto
Formaat: artículo original
Publicatiedatum:2019
Omschrijving: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.
Land:Kérwá
Instelling:Universidad de Costa Rica
Repositorio:Kérwá
Taal:Inglés
OAI Identifier:oai:kerwa.ucr.ac.cr:10669/83429
Online toegang:https://revistas.ucr.ac.cr/index.php/matematica/article/view/39931
https://hdl.handle.net/10669/83429
Keyword:Mosquito-borne diseases
Dengue
Climate variables
Costa Rica
Generalized additive models
Random forests
Enfermedades de trasmisión vectorial
Variables climáticas
Modelos aditivos generalizados
Bosques aleatorios