  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.
