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| Modeling of influenza-like illness prediction based on Elman neural network |
| ZHANG Tao*, GUAN Hai-bin, LI Fu-dong, HE Fan
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| * Department of Public Health Monitoring,Zhejiang Provincial Center for Diseases Control and Prevention,Hangzhou,Zhejiang 310051,China |
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Abstract Objective To build a model for influenza-like illness（ILI）prediction based on Elman neural network and to provide evidence for early warning of influenza epidemic in Zhejiang Province. Methods The data of ILI from 11 sentinel hospitals,influenza pathogen detection,meteorological factors and air pollutants in Zhejiang Province from 2013 to 2014 were collected. Time-delay correlation analysis was conducted to select variables for modeling. Based on Elman neural network,data from the 14th week of 2013 to the 44th week of 2014 were used as a training set to establish the model and the data from 45th week to 52nd weeks of 2014 were used as a test set for the model performance. Results There were ILI reported every week during 2013 and 2014,with a total of 506 391. The percentage of ILI cases per week was（3.07 ± 0.73）%. Ten variables selected by time-delay correlation analysis were the weekly average values of atmospheric pressure（13 weeks in advance）,vapor pressure（11 weeks in advance）,temperature（9 weeks in advance）,SO2（5 weeks in advance）,NO2（5 weeks in advance）,CO（5 weeks in advance）,PM2.5（5 weeks in advance）,PM10（5 weeks in advance）,air quality index（5 weeks in advance）and positive rate of pathogen（1 weeks in advance）. Elman neural network（10-15-1-1）was selected as the optimal model,and the prediction performed well,with 10.58% as the mean error rate and 0.876 7 as the nonlinear correlation coefficient. Conclusion This study demonstrated that Elman neural network including variables of meteorological factors,air pollutants and the positive rate of pathogen performed well on the short-term prediction of ILI incidence.
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Received: 24 August 2018
Revised: 25 November 2018
Published: 18 January 2019
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