Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan

Abstract It remains challenging to forecast local, seasonal outbreaks of influenza. The goal of this study was to construct a computational model for predicting influenza incidence. We built two computational models including an Autoregressive Distributed Lag (ARDL) model and a hybrid model integrat...

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Autores principales:	Fei He, Zhi-jian Hu, Wen-chang Zhang, Lin Cai, Guo-xi Cai, Kiyoshi Aoyagi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f75423255de144eea84babad8de5ca88
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spelling	oai:doaj.org-article:f75423255de144eea84babad8de5ca882021-12-02T16:07:00ZConstruction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan10.1038/s41598-017-07475-32045-2322https://doaj.org/article/f75423255de144eea84babad8de5ca882017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07475-3https://doaj.org/toc/2045-2322Abstract It remains challenging to forecast local, seasonal outbreaks of influenza. The goal of this study was to construct a computational model for predicting influenza incidence. We built two computational models including an Autoregressive Distributed Lag (ARDL) model and a hybrid model integrating ARDL with a Generalized Regression Neural Network (GRNN), to assess meteorological factors associated with temporal trends in influenza incidence. The modelling and forecasting performance of these two models were compared using observations collected between 2006 and 2015 in Nagasaki Prefecture, Japan. In both the training and forecasting stages, the hybrid model showed lower error rates, including a lower residual mean square error (RMSE) and mean absolute error (MAE) than the ARDL model. The lag of log-incidence, weekly average barometric pressure, and weekly average of air temperature were 4, 1, and 3, respectively in the ARDL model. The ARDL-GRNN hybrid model can serve as a tool to better understand the characteristics of influenza epidemic, and facilitate their prevention and control.Fei HeZhi-jian HuWen-chang ZhangLin CaiGuo-xi CaiKiyoshi AoyagiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fei He Zhi-jian Hu Wen-chang Zhang Lin Cai Guo-xi Cai Kiyoshi Aoyagi Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan
description	Abstract It remains challenging to forecast local, seasonal outbreaks of influenza. The goal of this study was to construct a computational model for predicting influenza incidence. We built two computational models including an Autoregressive Distributed Lag (ARDL) model and a hybrid model integrating ARDL with a Generalized Regression Neural Network (GRNN), to assess meteorological factors associated with temporal trends in influenza incidence. The modelling and forecasting performance of these two models were compared using observations collected between 2006 and 2015 in Nagasaki Prefecture, Japan. In both the training and forecasting stages, the hybrid model showed lower error rates, including a lower residual mean square error (RMSE) and mean absolute error (MAE) than the ARDL model. The lag of log-incidence, weekly average barometric pressure, and weekly average of air temperature were 4, 1, and 3, respectively in the ARDL model. The ARDL-GRNN hybrid model can serve as a tool to better understand the characteristics of influenza epidemic, and facilitate their prevention and control.
format	article
author	Fei He Zhi-jian Hu Wen-chang Zhang Lin Cai Guo-xi Cai Kiyoshi Aoyagi
author_facet	Fei He Zhi-jian Hu Wen-chang Zhang Lin Cai Guo-xi Cai Kiyoshi Aoyagi
author_sort	Fei He
title	Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan
title_short	Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan
title_full	Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan
title_fullStr	Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan
title_full_unstemmed	Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan
title_sort	construction and evaluation of two computational models for predicting the incidence of influenza in nagasaki prefecture, japan
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f75423255de144eea84babad8de5ca88
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_version_	1718384816767893504

Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan

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