Modeling the temporal pattern of Dengue, Chicungunya and Zika vector using satellite data and neural networks

The present work includes the temporal modeling of the oviposition activity of the Aedes aegypti mosquito, a vector of viral diseases such as Dengue, Chicungunya and Zika, based on time series of data extracted from earth observation satellite images. Unlike previous works, Machine Learning techniques that are capable of capturing nonlinear relationships between variables, such as artificial neural networks, are used to build a predictive model of the oviposition activity. The results of this model show a significant increase in performance compared to linear models trained with similar data sets.