基于状态空间的误差–趋势–季节模型在河南省肺结核发病率预测中的应用

目的 探索基于状态空间的误差–趋势–季节（ETSBSS）模型在河南省肺结核（TB）发病预测中的应用。方法 采用时间序列分解法解析2006—2019年河南省TB的趋势和季节组分。将数据分为训练（2006—2018年）和测试集（2019年），然后使用ETSBSS模型进行拟合和预测，并将模型性能与季节性求和自回归滑动平均混合（SARIMA）模型进行比较。结果 ETSBSS(A,MD,M)和SARIMA(1,0,0)(0,1,0)12模型被选择为预测河南省TB发病的最优模型。两种模型在训练集上拟合的平均绝对百分比误差（MAPE）依次为ETSBSS模型（5.65%）

Application of error-trend-seasonality model based on state-space in predicting tuberculosis incidence in Henan

  Objective  To evaluate the application of error-trend-seasonality model based on state-space (ETSBSS) in forecasting tuberculosis (TB) incidence in Henan province.   Methods  Time series decomposition method was used to analyze the trend and seasonal components of the TB incidence in Henan from 2006 to 2019. The data were divided into training set (2006?2018) and testing sets (2019), and then ETSBSS model was used for fitting and prediction, and the model’s fitting and prediction performances were compared with those of the seasonal autoregressive integrated moving average (SARIMA) model.   Results  The ETSBSS (A, MD, M) and SARIMA (1, 0, 0) (0, 1, 0)₁₂ specifications were selected as the best models to predict the TB incidence in Henan. The mean absolute percentage error (MAPE) values from the ETSBSS model were 5.65% on the training set and 4.61% on the testing set, which were lower than those from the SARIMA model (5.71% on the training set and 6.67% on the testing set). The values of mean absolute error, root mean square error, mean error rate, and root mean square percentage error also indicated that the fitting and prediction error rates of the ETSBSS model was lower than those of the SARIMA model, especially in the prediction set.   Conclusion  ETSBSS (A, MD, M) model shows a high prediction performance for the TB incidence in Henan, and it can be used as an effective decision-making tool to predict and analyze the dynamical epidemic patterns of TB in Henan.