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Title: An Integrated Recurrent Neural Network and Regression Model with Spatial and Climatic Couplings for Vector-borne Disease Dynamics
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 leishmaniasis data in Sri Lanka from 2013-2018 where infection outbreaks occurred. Our model out-performed ARIMA models across a number of regions with high infections, and an associated ablation study renders support to our modeling hypothesis and ideas.
Authors:
; ;
Award ID(s):
1924548 1952644
Publication Date:
NSF-PAR ID:
10339405
Journal Name:
International Conference on Pattern Recognition Applications and Methods
Page Range or eLocation-ID:
505 to 510
Sponsoring Org:
National Science Foundation
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