Hybrid modeling and prediction of oyster norovirus outbreaks

This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing data. Specifically, 10 years (20...

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Autores principales:	Shima Shamkhali Chenar, Zhiqiang Deng
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ann model norovirus outbreak oyster pca remote sensing Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/f8d3c743924440979b5e000fa1efb0a8
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spelling	oai:doaj.org-article:f8d3c743924440979b5e000fa1efb0a82021-11-06T06:03:55ZHybrid modeling and prediction of oyster norovirus outbreaks1477-89201996-782910.2166/wh.2021.251https://doaj.org/article/f8d3c743924440979b5e000fa1efb0a82021-04-01T00:00:00Zhttp://jwh.iwaponline.com/content/19/2/254https://doaj.org/toc/1477-8920https://doaj.org/toc/1996-7829This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing data. Specifically, 10 years (2007–2016) of cloud-free MODIS Aqua data for water leaving reflectance and environmental data were extracted from the center of each oyster harvest area. Then, the PCA was utilized to compress the size of the MODIS Aqua data. An ANN model was trained using the first 4 years of the data from 2007 to 2010 and validated using the additional 6 years of independent datasets collected from 2011 to 2016. Results indicated that the hybrid PCA-ANN model was capable of reproducing the 10 years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with a sensitivity of 72.7% and specificity of 99.9%, respectively, demonstrating the efficacy of the hybrid model. HIGHLIGHTS A hybrid model is presented for the prediction of oyster norovirus outbreaks.; The model is based on Artificial Neural Networks and Principal Component Analysis.; The model greatly expands the spatial coverage of oyster safety monitoring programs.; The model expands the water quality monitoring frequency from 1 month to 1 day.; The model input data are satellite remote-sensing data that are freely available.;Shima Shamkhali ChenarZhiqiang DengIWA Publishingarticleann modelnorovirusoutbreakoysterpcaremote sensingPublic aspects of medicineRA1-1270ENJournal of Water and Health, Vol 19, Iss 2, Pp 254-266 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ann model norovirus outbreak oyster pca remote sensing Public aspects of medicine RA1-1270
spellingShingle	ann model norovirus outbreak oyster pca remote sensing Public aspects of medicine RA1-1270 Shima Shamkhali Chenar Zhiqiang Deng Hybrid modeling and prediction of oyster norovirus outbreaks
description	This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing data. Specifically, 10 years (2007–2016) of cloud-free MODIS Aqua data for water leaving reflectance and environmental data were extracted from the center of each oyster harvest area. Then, the PCA was utilized to compress the size of the MODIS Aqua data. An ANN model was trained using the first 4 years of the data from 2007 to 2010 and validated using the additional 6 years of independent datasets collected from 2011 to 2016. Results indicated that the hybrid PCA-ANN model was capable of reproducing the 10 years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with a sensitivity of 72.7% and specificity of 99.9%, respectively, demonstrating the efficacy of the hybrid model. HIGHLIGHTS A hybrid model is presented for the prediction of oyster norovirus outbreaks.; The model is based on Artificial Neural Networks and Principal Component Analysis.; The model greatly expands the spatial coverage of oyster safety monitoring programs.; The model expands the water quality monitoring frequency from 1 month to 1 day.; The model input data are satellite remote-sensing data that are freely available.;
format	article
author	Shima Shamkhali Chenar Zhiqiang Deng
author_facet	Shima Shamkhali Chenar Zhiqiang Deng
author_sort	Shima Shamkhali Chenar
title	Hybrid modeling and prediction of oyster norovirus outbreaks
title_short	Hybrid modeling and prediction of oyster norovirus outbreaks
title_full	Hybrid modeling and prediction of oyster norovirus outbreaks
title_fullStr	Hybrid modeling and prediction of oyster norovirus outbreaks
title_full_unstemmed	Hybrid modeling and prediction of oyster norovirus outbreaks
title_sort	hybrid modeling and prediction of oyster norovirus outbreaks
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/f8d3c743924440979b5e000fa1efb0a8
work_keys_str_mv	AT shimashamkhalichenar hybridmodelingandpredictionofoysternorovirusoutbreaks AT zhiqiangdeng hybridmodelingandpredictionofoysternorovirusoutbreaks
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Hybrid modeling and prediction of oyster norovirus outbreaks

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