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Application of time series generalized regression neural network model in predicting the incidence of viral hepatitis
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Ya-jun SUN1, 3, Tian LIU2, Yuan YAO3
Modern Preventive Medicine | 2024, 51(23) : 4260 - 4265
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Modern Preventive Medicine | 2024, 51(23): 4260-4265
• Epidemiology and Statistical Methods •
Application of time series generalized regression neural network model in predicting the incidence of viral hepatitis
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Ya-jun SUN1, 3, Tian LIU2, Yuan YAO3
Affiliations
  • The Third People’s Hospital of Zhuhai, Zhuhai, Guangdong 519000, China
Published: 2024-12-10 doi: 10.20043/j.cnki.MPM.202406385
Outline
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Objective

To introduce the application of the time series generalized regression neural network (GRNN) model in predicting the incidence of viral hepatitis in China and to evaluate its fitting and predictive accuracy.

Methods

Monthly incidence data of viral hepatitis from 2004 to 2019 were collected to construct time series. Data from January 2004 to June 2019 were used as training data, while data from July to December 2019 served as testing data. Both GRNN and SARIMA models were established to predict the incidence from July to December 2019, and the predictions were compared with the testing data. The mean absolute percentage error (MAPE) was employed to assess the model’s fitting and predictive performance.

Results

The fitting MAPE for the GRNN model across various types of hepatitis ranged from 1.67% to 21.22%, while the predictive MAPE ranged from 2.26% to 17.17%. In comparison, the SARIMA model’s fitting MAPE for various types of hepatitis ranged from 3.84% to 7.87%, with a predictive MAPE ranging from 2.54% to 48.89%. Notably, the predictive MAPE for hepatitis A was 48.89%, indicating a significant prediction error.

Conclusion

The GRNN model outperformed the SARIMA model in predicting the monthly incidence of viral hepatitis in China, suggesting its suitability for broader application.

GRNN  /  SARIMA  /  Prediction  /  Viral hepatitis  /  Time series
Ya-jun SUN, Tian LIU, Yuan YAO. Application of time series generalized regression neural network model in predicting the incidence of viral hepatitis[J]. Modern Preventive Medicine, 2024 , 51 (23) : 4260 -4265 . DOI: 10.20043/j.cnki.MPM.202406385
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Year 2024 volume 51 Issue 23
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Article Info
doi: 10.20043/j.cnki.MPM.202406385
  • Receive Date：2024-06-22
  • Online Date：2026-03-18
  • Published：2024-12-10
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  • Received：2024-06-22
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    The Third People’s Hospital of Zhuhai, Zhuhai, Guangdong 519000, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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