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Due to the rapid geographic spread of the Aedes mosquito and the increase indengue incidence, dengue fever has been an increasing concern for public healthauthorities in tropical and subtropical countries worldwide. Significantchallenges such as climate change, the burden on health systems, and the riseof insecticide resistance highlight the need to introduce new andcost-effective tools for developing public health interventions. Various andlocally adapted statistical methods for developing climate-based early warningsystems have increasingly been an area of interest and research worldwide.Costa Rica, a country with micro-climates and endemic circulation of the denguevirus (DENV) since 1993, provides ideal conditions for developing projectionmodels with the potential to help guide public health efforts and interventionsto control and monitor future dengue outbreaks.

Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica