Modelling spatio-temporal data of dengue fever using generalized additive mixed models.

Epidemiological studies have revealed a complex association between weather and dengue transmission. Our aim is the development of a Spatio-temporal modelling of dengue fever via a Generalized Additive mixed model (GAMM). The structure is based on unknown smoother functions for climatic and a set of non-climatic covariates. All the climatic covariates were found statistically significant with optimal lagged effect and the smoothed curves fairly captured the real dynamic on dengue fever. It was also found that critical levels of dengue cases were reached with temperature between 26 °C and 30 °C. The findings also revealed for the first time that the El Niño phenomenon fluctuating between 26.5 °C and 28.0 °C had the worse impact on dengue transmission. This study brings together a large dataset from different sources including Ministry of Health from Venezuela. It was also benefited from a remote satellite climatic data provided by the National Aeronautics and Space Administration (NASA).