Abstract

VASQUEZ, Paola; LORIA, Antonio; SANCHEZ, Fabio  and  BARBOZA, Luis A.. Climate-driven statistical models as effective predictors of local dengue incidence in costa rica: a generalized additive model and random forest approach. Rev. Mat [online]. 2020, vol.27, n.1, pp.1-22. ISSN 1409-2433.  http://dx.doi.org/10.15517/rmta.v27i1.39931.

Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country.

Keywords : mosquito-borne diseases; dengue; climate variables; Costa Rica; generalized additive models; random forests.

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