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

Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study. Author summaryDengue fever is a vector-borne viral disease endemic to tropical and subtropical countries. The virus is transmitted by female Aedes mosquitoes and affects approximately 100 million people every year. Although most infections are mild or asymptomatic, some may cause severe symptoms, leading to a higher risk of death. In the affected countries, the challenges associated with preventing and controlling dengue outbreaks have highlighted the need for novel tools. In this context, using statistical tools with climate and epidemiological information makes it possible to provide timely information to public health officials about the risk of dengue outbreaks, allowing the optimization of resources and preventive and non-reactive decision-making.