基于组合模型的太原市丙型肝炎发病趋势预测研究

郭思瑶; 赵启玉; 张越; 张萍; 车晓文; 郑金鸽; 王蕾

Abstract

郭思瑶,赵启玉,张越,张萍,车晓文,郑金鸽,王蕾.基于组合模型的太原市丙型肝炎发病趋势预测研究[J].Chinese journal of Epidemiology,2025,46(2):204-209

基于组合模型的太原市丙型肝炎发病趋势预测研究

Research on the prediction of Hepatitis C incidence trend in Taiyuan City based on combination model

Received:August 14, 2024

DOI：10.3760/cma.j.cn112338-20240814-00502

KeyWord: 丙型肝炎差分自回归移动平均模型反向传播神经网络组合模型发病趋势

English Key Word: Hepatitis C Autoregressive integrated moving average model Back propagation neural network Combination model Incidence trend

FundProject:山西省青年科学研究基金（201901D211326，202103021223216）；山西省高等教育“百亿工程”科技引导专项（BYBLD002）；山西省太原市医学重点学科（2024-2046）

Author Name	Affiliation	E-mail
Guo Siyao	School of Medical Sciences, Shanxi Medical University, Taiyuan 030001, China Key Laboratory of Coal Environmental Pathogenesis and Prevention, Ministry of Education, Taiyuan 030001, China Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China Research Center of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan 030001, China
Zhao Qiyu	School of Medical Sciences, Shanxi Medical University, Taiyuan 030001, China Key Laboratory of Coal Environmental Pathogenesis and Prevention, Ministry of Education, Taiyuan 030001, China Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China Research Center of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan 030001, China Department of Radiation Health, Taiyuan Center for Disease Control and Prevention, Taiyuan 030012, China	maradona164@163.com
Zhang Yue	Key Laboratory of Coal Environmental Pathogenesis and Prevention, Ministry of Education, Taiyuan 030001, China Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China Research Center of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan 030001, China
Zhang Ping	Key Laboratory of Coal Environmental Pathogenesis and Prevention, Ministry of Education, Taiyuan 030001, China Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China Research Center of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan 030001, China
Che Xiaowen	Department of AIDS Protection and Control, Taiyuan Center for Disease Control and Prevention, Taiyuan 030012, China
Zheng Jinge	Department of AIDS Protection and Control, Taiyuan Center for Disease Control and Prevention, Taiyuan 030012, China
Wang Lei	Department of AIDS Protection and Control, Taiyuan Center for Disease Control and Prevention, Taiyuan 030012, China

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Abstract:

目的根据差分自回归移动平均（ARIMA）模型、反向传播神经网络（BPNN）、ARIMA-BPNN模型，针对太原市丙型肝炎（丙肝）发病数据特征选择适合发病趋势预测的最优模型。方法选取2008-2021年现住址为太原市的丙肝报告病例数据，使用季节性趋势分解图分析期间太原市丙肝月发病率的季节性特征，并建立ARIMA模型、BPNN模型、ARIMA-BPNN模型进行预测。采用平均绝对误差（MAE）、均方误差（MSE）、均方根误差（RMSE）以及平均绝对百分比误差（MAPE）指标衡量模型的性能。结果累计报告丙肝病例20 025例，总体发病趋势稳定。BPNN模型在MSE、MAE和RMSE指标上表现较好，ARIMA-BPNN模型在MAPE指标上表现较好，ARIMA模型表现相对一般。结论 ARIMA-BPNN模型是预测太原市丙肝发病趋势的较优模型，预测性能高于单一模型，在传染病发病趋势预测中有重大前景。

English 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.

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