Revista de Matemática: Teoría y Aplicaciones ISSN Impreso: 1409-2433 ISSN electrónico: 2215-3373

OAI: https://revistas.ucr.ac.cr/index.php/matematica/oai
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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Keywords

mosquito-borne diseases
dengue
climate variables
Costa Rica
generalized additive models
random forests
enfermedades de trasmisión vectorial
dengue
variables climáticas
Costa Rica
modelos aditivos generalizados
bosques aleatorios

How to Cite

Vásquez, P., Loría, A., Sánchez, F., & Barboza, L. A. (2019). Climate-driven statistical models as effective predictors of local dengue incidence in costa rica: a generalized additive model and random forest approach. Revista De Matemática: Teoría Y Aplicaciones, 27(1), 1–21. https://doi.org/10.15517/rmta.v27i1.39931

Abstract

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.

https://doi.org/10.15517/rmta.v27i1.39931
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