Objective To study the application of autoregressive integrated moving average (ARIMA) model to predict the monthly reported malaria cases in China, so as to provide a reference for prevention and control of malaria. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported malaria cases of the time series of 20062015 and 2011-2015, respectively. The data of malaria cases from January to December, 2016 were used as validation data to compare the accuracy of the two ARIMA models. Results The models of the monthly reported cases of malaria in China were ARIMA (2, 1, 1) (1, 1, 0)12 and ARIMA (1, 0, 0) (1, 1, 0)12 respectively. The comparison between the predictions of the two models and actual situation of malaria cases showed that the ARIMA model based on the data of 2011-2015 had a higher accuracy of forecasting than the model based on the data of 2006-2015 had. Conclusion The establishment and prediction of ARIMA model is a dynamic process, which needs to be adjusted unceasingly according to the accumulated data, and in addition, the major changes of epidemic characteristics of infectious diseases must be considered.
［摘要］目的 采用自回归移动平均 (Autoregressive integrated moving average, ARIMA)模型对全国 (不含港澳台地区)疟疾月报告病例数进行预测研究, 为疟疾的预防控制提供参考依据。方法 通过SPSS 24.0软件, 建立两个时间序列, 分别为2006–2015年和2011–2015年全国疟疾月报告病例数的时间序列, 并建立最优ARIMA模型, 以2016年1–12月全国疟疾月报告病例数进行验证。结果 2006–2015、2011–2015年两个不同时间序列建立的全国疟疾月报告病例数模型分别为ARIMA (2, 1, 1) (1, 1, 0)12和ARIMA (1, 0, 0) (1, 1, 0)12, 分别对2016年1–12月数据进行预测, 基于2011–2015年数据建立的ARIMA模型的预测误差更小。结论 模型的建立和预测应用是动态过程, 需要不断根据积累的数据进行调整, 从而提高预测精度, 但同时要充分考虑传染病流行特征的重大变化等其他因素。 ［关键词］疟疾; 自回归移动平均模型; 预测.
Keywords: Autoregressive integrated moving average (ARIMA)model; Malaria; Prediction.