关键词: 肾综合征出血热, 预测, 指数平滑法, 季节性差分自回归滑动平均模型, 最小绝对值收缩与选择算子算法回归

Abstract: Objective To compare the performance of three time series models in predicting the incidence of hemorrhagic fever with renal syndrome (HFRS),and to explore the predictive performance of a modified seasonal autoregressive integrated moving average (SARIMAX) model with independent variables introduced from a least absolute shrinkage and selection operator (LASSO) model. Methods The information on HFRS incidence, rodent density, meteorological and socio-economic data in Guangzhou,China from 2006 to 2022 were systematically collected. Exponential smoothing (ETS), SARIMAX, and LASSO-SARIMAX models were constructed to predict the incidence of HFRS. Autocorrelation function (ACF), mean percentage error (MPE), and mean absolute percentage error (MAPE) were used to evaluate the predictive effects of the models. MAPE was used to compare the prediction effects of the three models in different prediction times. Results The mean annual incidence rate of HFRS in Guangzhou from 2006 to 2022 was 0.06/100 000. The MAPE for the training set was 45.066 for the ETS model, 51.403 for the SARIMA model,and 39.466 for the LASSO-SARIMAX model. The LASSO-SARIMAX model had the lowest MAPE in the training data set at a prediction length of 12 months,with a lower MAPE compared with the ETS model at a length of 24 months. Conclusion The LASSO-SARIMAX model shows good performance in predicting the incidence of HFRS in Guangzhou in the short and medium term.

Key words: Hemorrhagic fever with renal syndrome (HFRS), Predict, Exponential smoothing (ETS) method, Seasonal differential autoregressive integrated moving average (SARIMA) model, Least absolute shrinkage and selection operator (LASSO)