Abstract:

Objective To explore the feasibility of the application of time series model to predicting the malaria incidence in China so as to provide the theoretical basis for the reduction of malaria incidence. Methods An autoregressive integrated seasonal moving average (ARIMA) model was established based on the data on malaria incidences from 2004 to 2009 in China. With the difference method used to smooth the sequence, we determined the order and established the 2010 national malaria incidence forecast model to evaluate the predicting results. Results It was found that there was a year-by-year decrease in the incidence and death rate of malaria from 2004 to 2009, and the incidence fluctuated with seasons with the peak incidence seen from July to October. Seasonal ARIMA model fitted well, and the residual autocorrelation function graph showed that the residuals were white noise sequences, which fitted well with the incidence sequences in the previous periods of time. Further prediction of the incidences in the individual months in 2010 with the model indicated that malaria incidence decreased in the year compared with the previous years. Conclusion The seasonal ARIMA model can be well applied to the fitting of data on malaria incidence with good forecasting, providing a scientific basis for the development of measures for the prevention and control of malaria.

Key words: Time series analysis, Seasonal ARIMA model, Forecasting, Malaria, Incidence

摘要：

目的 探讨应用时间序列季节滑动平均混合模型法(seasonal ARIMA)进行疟疾发病率预测的可行性，为降低疟疾发病率提供理论依据。方法 收集全国2004－2009年疟疾发病率数据，建立数据库。采用差分方法对序列资料进行平稳化及定阶，建立2010年全国疟疾发病率数据的序列分析预测模型，并对预测结果进行分析和评价。结果 2004－2009年疟疾发病例数和死亡例数呈逐年下降趋势，其发病呈明显的季节性趋势，每年的7－10月为该病的发病高峰期。季节ARIMA模型拟合结果较理想，残差序列的自相关函数图显示残差均为白噪声序列，其很好地拟合了既往时间段上的发病率序列，进一步用该模型对2010年各月的发病率进行预测，提示2010年全目的国疟疾发病较临近年份有所下降，应继续巩固加强防治效果。结论 用季节ARIMA模型对疟疾发病率数据拟合较为满意，预测效果良好，可为进一步制定预防措施提供依据。

关键词: 时间序列分析, 季节滑动平均混合模型法, 预测, 疟疾, 发病率