Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population
Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population
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DOI：
10.2147/idr.s190418
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发表时间：
2019-01-01
影响因子：
3.9
通讯作者：
Wang, Jianming
中科院分区：
文献类型：
--
作者：
Li, Zhongqi;Wang, Zhizhong;Wang, Jianming
目的：通过比较自回归积分移动平均（ARIMA）模型和ARIMA-广义回归神经网络（GRNN）混合模型的预测值，探讨适合中国人群结核病（TB）的预测模型。我们使用连云港市2007年1月至2016年6月的结核病月发病率构建拟合模型，并利用2016年7月至2016年12月的发病率来评估预测准确性。用均方根误差（RMSE）、平均绝对误差百分比（MAPE）、平均绝对误差（MAE）和平均误差率（MER）评价模型对结核病发病率的拟合和预测性能。从ARIMA模型中选择ARIMA（10，1，0）（0，1，1）（12）模型，ARIMA-GRNN混合模型的最优扩散值为0.23。对于拟合数据集，ARIMA（10，1，0）（0，1，1）（12）模型的RMSE，MAPE，MAE和MER分别为0.5594，11.5000，0.4202和0.1132，ARIMA-GRNN混合模型分别为0.5259，11.2181，0.3992和0.1075。对于预测数据集，ARIMA的RMSE、MAPE、MAE和MER分别为0.2805、8.8797、0.2261和0.0851（10，1，0）（0，1，1）（12）模型，ARIMA-GRNN混合模型分别为0.2553，5.7222，0.1519和0.0571。结果表明，ARIMA-GRNN混合模型对中国人群结核病发病率的短期预测效果上级单一ARIMA模型，特别是对发病率的高峰和低谷的拟合和预测效果更好。
Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model.Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB.Results: The ARIMA (10, 1, 0) (0, 1, 1)(12) model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)(12) model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)(12) model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model.Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.