An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics
Abstract
We developed an integrated recurrent neural network and nonlinear regression spatio-temporal model for vector-borne disease evolution. We take into account climate data and seasonality as external factors that correlate with disease transmitting insects (e.g. flies), also spill-over infections from neighboring regions surrounding a region of interest. The climate data is encoded to the model through a quadratic embedding scheme motivated by recommendation systems. The neighboring regions' influence is modeled by a long short-term memory neural network. The integrated model is trained by stochastic gradient descent and tested on leish-maniasis data in Sri Lanka from 2013-2018 where infection outbreaks occurred. Our model outperformed ARIMA models across a number of regions with high infections, and an associated ablation study renders support to our modeling hypothesis and ideas.
Keywords
Cite
@article{arxiv.2201.09394,
title = {An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics},
author = {Zhijian Li and Jack Xin and Guofa Zhou},
journal= {arXiv preprint arXiv:2201.09394},
year = {2022}
}