Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques
This work addresses public health planning for dengue outbreaks in Costa Rica, but it is incremental as it applies existing methods to a specific region.
The study tackled dengue fever risk prediction in Costa Rica by using climate variables with GAMLSS and Random Forest models, achieving reliable predictions with measured uncertainty to aid resource allocation before outbreaks.
Dengue fever is a vector-borne disease mostly endemic to tropical and subtropical countries that affect millions every year and is considered a significant burden for public health. Its geographic distribution makes it highly sensitive to climate conditions. Here, we explore the effect of climate variables using the Generalized Additive Model for location, scale, and shape (GAMLSS) and Random Forest (RF) machine learning algorithms. Using the reported number of dengue cases, we obtained reliable predictions. The uncertainty of the predictions was also measured. These predictions will serve as input to health officials to further improve and optimize the allocation of resources prior to dengue outbreaks.