Dengue fever is a global health problem. Probing its spatio-temporal characteristics and building corresponding spatio-temporal models from geographical analyses may constitute a novel approach to its effective prevention and control. Spatio-temporal data mining and spatio-temporal process model-building were used to examine spatial relationships between dengue fever and environmental

climate

geographical

and population factors

as well as spatial autocorrelation of dengue fever cases

aiming to identify influencing factors of dengue fever. A Genetic Algorithm (GA) was introduced to overcome the weakness of Back Propagation (BP) neural network models

which is typically subject to local optima. The improved model was applied to spatio-temporal simulation of dengue fever. the spatio-temporal diffusion of dengue fever was significantly associated with temperature

humidity

residential areas

traffic

and population density. A remarkable spatial autocorrelation was found amongst the dengue fever cases. Nonlinear relationships were revealed between the occurrence and diffusion of dengue fever and environmental

climate

geographical

and population factors. The improved GA-BP neural network model may enhance the accuracy of simulation of spatio-temporal diffusion of dengue fever (RMSE=0.081). The occurrence and diffusion of dengue fever were affected by multiple factors. The GA-BP neural network model can effectively simulate spatio-temporal processes of dengue fever. The improved model could likewise apply to simulations of other aedes-related diseases.