The predictive skill of convolutional neural networks models for disease forecasting.

In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a h...

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Autores principales:	Kookjin Lee, Jaideep Ray, Cosmin Safta
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9a39ca67b96c4b9d9f34bda94007bbe8
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spelling	oai:doaj.org-article:9a39ca67b96c4b9d9f34bda94007bbe82021-12-02T20:09:18ZThe predictive skill of convolutional neural networks models for disease forecasting.1932-620310.1371/journal.pone.0254319https://doaj.org/article/9a39ca67b96c4b9d9f34bda94007bbe82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254319https://doaj.org/toc/1932-6203In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a higher forecasting skill compared to conventional models such as ARIMA. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block-temporal convolutional networks and simple neural attentive meta-learners-for epidemiological forecasting. We then test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, plain RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.Kookjin LeeJaideep RayCosmin SaftaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254319 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kookjin Lee Jaideep Ray Cosmin Safta The predictive skill of convolutional neural networks models for disease forecasting.
description	In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a higher forecasting skill compared to conventional models such as ARIMA. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block-temporal convolutional networks and simple neural attentive meta-learners-for epidemiological forecasting. We then test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, plain RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.
format	article
author	Kookjin Lee Jaideep Ray Cosmin Safta
author_facet	Kookjin Lee Jaideep Ray Cosmin Safta
author_sort	Kookjin Lee
title	The predictive skill of convolutional neural networks models for disease forecasting.
title_short	The predictive skill of convolutional neural networks models for disease forecasting.
title_full	The predictive skill of convolutional neural networks models for disease forecasting.
title_fullStr	The predictive skill of convolutional neural networks models for disease forecasting.
title_full_unstemmed	The predictive skill of convolutional neural networks models for disease forecasting.
title_sort	predictive skill of convolutional neural networks models for disease forecasting.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9a39ca67b96c4b9d9f34bda94007bbe8
work_keys_str_mv	AT kookjinlee thepredictiveskillofconvolutionalneuralnetworksmodelsfordiseaseforecasting AT jaideepray thepredictiveskillofconvolutionalneuralnetworksmodelsfordiseaseforecasting AT cosminsafta thepredictiveskillofconvolutionalneuralnetworksmodelsfordiseaseforecasting AT kookjinlee predictiveskillofconvolutionalneuralnetworksmodelsfordiseaseforecasting AT jaideepray predictiveskillofconvolutionalneuralnetworksmodelsfordiseaseforecasting AT cosminsafta predictiveskillofconvolutionalneuralnetworksmodelsfordiseaseforecasting
_version_	1718375079653408768

The predictive skill of convolutional neural networks models for disease forecasting.

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