Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

Abstract Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Behnam Nikparvar, Md. Mokhlesur Rahman, Faizeh Hatami, Jean-Claude Thill
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac55c202cf834ba3a5d5637020e57b19
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ac55c202cf834ba3a5d5637020e57b19
record_format	dspace
spelling	oai:doaj.org-article:ac55c202cf834ba3a5d5637020e57b192021-11-08T10:47:45ZSpatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network10.1038/s41598-021-01119-32045-2322https://doaj.org/article/ac55c202cf834ba3a5d5637020e57b192021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01119-3https://doaj.org/toc/2045-2322Abstract Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.Behnam NikparvarMd. Mokhlesur RahmanFaizeh HatamiJean-Claude ThillNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Behnam Nikparvar Md. Mokhlesur Rahman Faizeh Hatami Jean-Claude Thill Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network
description	Abstract Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.
format	article
author	Behnam Nikparvar Md. Mokhlesur Rahman Faizeh Hatami Jean-Claude Thill
author_facet	Behnam Nikparvar Md. Mokhlesur Rahman Faizeh Hatami Jean-Claude Thill
author_sort	Behnam Nikparvar
title	Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network
title_short	Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network
title_full	Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network
title_fullStr	Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network
title_full_unstemmed	Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network
title_sort	spatio-temporal prediction of the covid-19 pandemic in us counties: modeling with a deep lstm neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ac55c202cf834ba3a5d5637020e57b19
work_keys_str_mv	AT behnamnikparvar spatiotemporalpredictionofthecovid19pandemicinuscountiesmodelingwithadeeplstmneuralnetwork AT mdmokhlesurrahman spatiotemporalpredictionofthecovid19pandemicinuscountiesmodelingwithadeeplstmneuralnetwork AT faizehhatami spatiotemporalpredictionofthecovid19pandemicinuscountiesmodelingwithadeeplstmneuralnetwork AT jeanclaudethill spatiotemporalpredictionofthecovid19pandemicinuscountiesmodelingwithadeeplstmneuralnetwork
_version_	1718442652233367552

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

Ejemplares similares