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Dengue fever is a vector-borne disease mostly endemic to tropical andsubtropical countries that affect millions every year and is considered asignificant burden for public health. Its geographic distribution makes ithighly sensitive to climate conditions. Here, we explore the effect of climatevariables using the Generalized Additive Model for location, scale, and shape(GAMLSS) and Random Forest (RF) machine learning algorithms. Using the reportednumber of dengue cases, we obtained reliable predictions. The uncertainty ofthe predictions was also measured. These predictions will serve as input tohealth officials to further improve and optimize the allocation of resourcesprior to dengue outbreaks.

Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques