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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2017-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-017-07475-3 |
| _version_ | 1818995109038915584 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T21:08:37Z |
| format | Article |
| id | doaj.art-f75423255de144eea84babad8de5ca88 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-20T21:08:37Z |
| publishDate | 2017-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f75423255de144eea84babad8de5ca882022-12-21T19:26:34ZengNature PortfolioScientific Reports2045-23222017-08-01711910.1038/s41598-017-07475-3Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, JapanFei He0Zhi-jian Hu1Wen-chang Zhang2Lin Cai3Guo-xi Cai4Kiyoshi Aoyagi5Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical UniversityFujian Province Key Laboratory of Environment and Health, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical UniversityInstitute of Tropical Medicine, Nagasaki UniversityDepartment of Public Health, Nagasaki University Graduate School of Biomedical SciencesAbstract 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.https://doi.org/10.1038/s41598-017-07475-3 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-017-07475-3 |
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