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

• URL: http://arxiv.org/abs/2201.09394v1
• Date: Sun, 23 Jan 2022 23:04:58 GMT
• Title: An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics
• Authors: Zhijian Li, Jack Xin, Guofa Zhou
• Abstract summary: We develop an integrated recurrent neural network and nonlinear regression model for disease-borne disease evolution. We take into account climate data seasonality and as external factors that correlate with insects (e.g. flies), also spill-over infections from neighboring regions surrounding a region of interest. The integrated model is trained by neuraltemporal descent and tested on leish-maniasis data in Sri Lanka from 2013-2018 where infection outbreaks occurred.
• Score: 4.254099382808598
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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