Climate-driven statistical models as effective predictors of local dengue incidence in Costa Rica: A Generalized Additive Model and Random Forest approach
This work addresses dengue prediction for public health in Costa Rica, but it is incremental as it applies existing methods to a specific dataset.
The study tackled predicting local dengue incidence in Costa Rica by using climate data from 2007-2017 to fit Generalized Additive Model and Random Forest models, resulting in retrospective predictions of relative risk across five municipalities.
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.