ARIMA、ARIMAX、NGO-LSTM模型在山东省聊城市结核病发病数预测中的应用

Application of ARIMA, ARIMAX, and NGO-LSTM models in forecasting the incidence of tuberculosis cases in Liaocheng City, Shandong Province

目的:通过比较差分自回归移动平均(autoregressive integrated moving average,ARIMA)模型、具有外生回归变量的差分自回归移动平均(autoregressive integrated moving average with exogenous regressors,ARIMAX)模型和结合北方苍鹰优化(Northern goshawk optimization,NGO)算法的长期短期记忆(long short-term memory,LSTM)神经网络模型确定预测山东省聊城市结核病发病数的最佳模型。方法:收集聊城市2011年1月至2018年12月结核病月发病数据,分别构建ARIMA、ARIMAX和NGO-LSTM模型,评估3种模型在预测2018年结核病发病数的表现。结果:ARIMA模型、考虑月平均相对湿度(滞后1个月)与最低温度(滞后2个月)的多变量ARIMAX模型和NGO-LSTM模型对2018年结核病发病数预测的平均绝对百分比误差分别为9.293%、8.419%、5.820%,平均绝对误差分别为19.282、16.997、13.119,均方根误差分别为23.773、22.191、16.297。结论:在3种模型中,NGO-LSTM模型对聊城市结核病月发病数的预测效果最好,为结核病预警系统的建立提供了一种新思路,可为有关部门针对结核病的预防及控制决策提供科学依据。

Objective:The purpose of this study was to determine the optimal model for predicting tuberculosis incidence in Liaocheng City,Shandong Province by comparing the Autoregressive Integrated Moving Average(ARIMA)model,the Autoregressive Integrated Moving Average with Exogenous Regressors(ARIMAX)model,and the Long Short-Term Memory(LSTM)model combined with the Northern Goshawk Optimization(NGO)algorithm.Methods:Monthly tuberculosis case data from January 2011 to December 2018 were collected.We constructed ARIMA model,ARIMAX model,and NGO-LSTM model based on data from January 2011 to December 2017,respectively,and evaluate the performance of the three models in predicting the number of tuberculosis cases in 2018.Results:The mean absolute percentage errors(MAPE)for the ARIMA model,the multivariate ARIMAX model considering the monthly average relative humidity(lagged by 1 month)and the minimum temperature(lagged by 2 months),and the NGO-LSTM model for predicting tuberculosis incidence in 2018 were 9.293%,8.419%,and 5.820%,respectively.The mean absolute errors(MAE)were 19.282,16.997,and 13.119,respectively,and the root mean square errors(RMSE)were 23.773,22.191,and 16.297,respectively.Conclusion:Among the three models,the NGO-LSTM model had the best predictive performance for monthly tuberculosis incidence in Liaocheng City,providing a new idea for the establishment of a tuberculosis alerting system and scientific basis for relevant departments to make decisions on tuberculosis prevention and control policy.