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

Corresponding author: ZHOU Yi, zhouyi@mail.sysu.edu.cn

DOI: 10.12201/bmr.202407.00046

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  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

  Submit time: 18 July 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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