Research on the prediction of Hepatitis C incidence trend in Taiyuan City based on combination model
10.3760/cma.j.cn112338-20240814-00502
- VernacularTitle:基于组合模型的太原市丙型肝炎发病趋势预测研究
- Author:
Siyao GUO
1
;
Qiyu ZHAO
;
Yue ZHANG
;
Ping ZHANG
;
Xiaowen CHE
;
Jinge ZHENG
;
Lei WANG
Author Information
1. 山西医科大学医学科学院，太原 030001
- Publication Type:Journal Article
- Keywords:
Hepatitis C;
Autoregressive integrated moving average model;
Back propagation neural network;
Combination model;
Incidence trend
- From:
Chinese Journal of Epidemiology
2025;46(2):204-209
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Based on the autoregressive integrated moving average (ARIMA) model, back propagation neutral network (BPNN), and ARIMA-BPNN model, select the optimal model suitable for predicting the incidence trend of hepatitis C in Taiyuan City according to the characteristics of the data.Methods:The data of reported cases of hepatitis C in Taiyuan from 2008 to 2021 were selected, and the seasonal trend decomposition chart was used to analyze the seasonal characteristics of the monthly incidence rate of hepatitis C in Taiyuan during the period, and the ARIMA model, BPNN model, and ARIMA-BPNN model were established to predict. The performance of the model was measured using four indicators: mean absolute error ( MAE), mean squared error ( MSE), root mean square error ( RMSE), and mean absolute percentage error ( MAPE). Results:A total of 20 025 cases of hepatitis C were reported, and the overall incidence trend was stable. The BPNN model performed well on MSE, MAE, and RMSE indicators, the ARIMA-BPNN model performed well on MAPE indicators, and the ARIMA model performed relatively averagely. Conclusions:The ARIMA-BPNN model is a better model for predicting the trend of hepatitis C in Taiyuan City, with a higher predictive performance than a single model. It has significant prospects in predicting the trend of infectious diseases.
