通讯作者: 周毅, zhouyi@mail.sysu.edu.cn

DOI：10.12201/bmr.202407.00046

Research on Hemorrhagic Fever with Renal Syndrome Incidence Prediction Based on the SARIMA-LSTM Model

• 摘要：目的/意义 探究前沿技术在肾综合征出血热（HFRS）发病率预测中的应用，梳理、组合多种时序分析方法，并评价模型效果，筛选最佳模型。方法/过程 利用2004-2020年全国HFRS发病率数据，分析基于统计学方法的SARIMA、STL-ARIMA、TBATS模型，基于神经网络的NNAR、LSTM模型，基于三种加权方式的SARIMA-LSTM组合模型的预测效果，利用RMSE、MAE、MAPE综合评价模型效果。结果/结论 SARIMA、LSTM为单一模型中较优模型，RMSE、MAE和MAPE分别为：0.01224、0.00981、18.43%，0.00998、0.00705、14.08%；SARIMA-LSTM组合模型相较单一模型效果均有提升，基于误差倒数法的SARIMA-LSTM组合模型为最优模型，三项评价指标值分别为：0.00940、0.00519、9.32%，筛选出的最佳模型以及组合策略，有望为HFRS发病预警系统模型设计提供技术支持与参考。

  关键词： 肾综合征出血热；传染病监测预警；统计学模型；机器学习；SARIMA-LSTM模型; ; ; ; 

   

  Abstract: Purpose/Significance To investigate the application of cutting-edge technologies in predicting the incidence of Hemorrhagic Fever with Renal Syndrome (HFRS), to compile and integrate various time-series analysis methods, and to evaluate model performance in selecting the optimal model. Method/Process Utilizing national HFRS incidence data from 2004 to 2020, the predictive effectiveness of models based on statistical methods: SARIMA, STL-ARIMA, and TBATS, neural network approaches: NNAR, LSTM, and combined models of SARIMA-LSTM with three different weighting schemes were analyzed. The performance of these models is comprehensively assessed using RMSE, MAE, and MAPE. Result/Conclusion The SARIMA and LSTM models are identified as the superior individual models, with their respective performance metrics—RMSE, MAE, and MAPE—recorded as follows: 0.01224, 0.00981, and 18.43% for SARIMA; 0.00998, 0.00705, and 14.08% for LSTM. The combined SARIMA-LSTM model demonstrates enhanced performance compared to individual models. The SARIMA-LSTM model optimized using the reciprocal of error method is deemed the optimal model, achieving significantly reduced error measures with values of 0.00940 for RMSE, 0.00519 for MAE, and 9.32% for MAPE. The selection of this optimal model and the strategic combination approach bodes well to offer technical support and guidance for the development of an early warning system model tailored to forecasting HFRS outbreaks.

  Key words: hemorrhagic fever with renal syndrome; infectious disease surveillance and early warning; statistical model; machine learning; SARIMA-LSTM model

  提交时间：2024-07-18

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• 序号	提交日期	编号	操作
  1	2024-05-30	bmr.202407.00046V1	下载

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