  Dengue fever is a mosquito-borne disease present in all Brazilian territory.
Brazilian government, however, lacks an accurate early warning system to
quickly predict future dengue outbreaks. Such system would help health
authorities to plan their actions and to reduce the impact of the disease in
the country. However, most attempts to model dengue fever use parametric models
which enforce a specific expected behaviour and fail to capture the inherent
complexity of dengue dynamics. Therefore, we propose a new Bayesian
non-parametric model based on Gaussian processes to design an accurate and
flexible model that outperforms previous/standard techniques and can be
incorporated into an early warning system, specially at cities from Southeast
and Center-West regions. The model also helps understanding dengue dynamics in
Brazil through the analysis of the covariance functions generated.
