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We developed an integrated recurrent neural network and nonlinear regressionspatio-temporal model for vector-borne disease evolution. We take into accountclimate data and seasonality as external factors that correlate with diseasetransmitting insects (e.g. flies), also spill-over infections from neighboringregions surrounding a region of interest. The climate data is encoded to themodel through a quadratic embedding scheme motivated by recommendation systems.The neighboring regions' influence is modeled by a long short-term memoryneural network. The integrated model is trained by stochastic gradient descentand tested on leish-maniasis data in Sri Lanka from 2013-2018 where infectionoutbreaks occurred. Our model outperformed ARIMA models across a number ofregions with high infections, and an associated ablation study renders supportto our modeling hypothesis and ideas.

An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics