# Climate-driven statistical models as effective predictors of local dengue incidence in Costa Rica: A Generalized Additive Model and Random Forest approach

## TL;DR

This study develops climate-based statistical models using GAM and Random Forest to predict dengue incidence in Costa Rica's diverse micro-climates, aiding targeted public health interventions.

## Contribution

It introduces a combined GAM and Random Forest modeling approach for predicting dengue risk based on climate data in Costa Rica.

## Key findings

- Models successfully predicted dengue risk in different municipalities.
- Climate variables significantly influence dengue incidence.
- The approach offers a tool for proactive public health planning.

## 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.