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目的探索基于季节性差分的自回归移动平均模型(ARIMA模型)在恙虫病预测应用的可行性。方法搜集中国疾病预防控制信息系统中的恙虫病发病资料,应用SPSS 17.0软件中的ARIMA模型,对北京市平谷区2010-2015年的恙虫病病例发病时间建立模型并拟合,根据模型对2016年的发病数做出预测。结果北京市平谷区恙虫病发病呈现逐年上升趋势,具有明显的季节性和周期性,每年的10月为发病高峰,经选取最优模型为ARIMA(1,2,2)(2,1,0)12,其平稳的R2=0.889,BIC=5.460,Ljung-Box Q检验,P=0.428,残差序列为白噪声序列。结论利用监测数据建立时间序列是预测传染病发展趋势的一个重要手段,此次建立的ARIMA模型对北京市平谷区恙虫病发病值及预测值拟合较好,可以作为恙虫病短期发病预测手段。
Objective To explore the feasibility of ARIMA model based on the seasonal difference in the prediction of tsutsugamushi disease. Methods The data of tsutsugamushi disease in Chinese CDC were collected and the onset time of tsutsugamushi disease in Pinggu District of Beijing from 2010 to 2015 was established and fitted using the ARIMA model of SPSS 17.0 software. According to the model, The incidence of the number to make a prediction. Results The incidence of tsutsugamushi disease in Pinggu District of Beijing showed a rising trend year by year with obvious seasonal and periodicity. The peak of the incidence was in October each year. The optimal model was ARIMA (1,2,2) (2,1,0 ) 12, its stable R2 = 0.889, BIC = 5.460, Ljung-Box Q test, P = 0.428, the residual sequence is white noise sequence. Conclusion The use of monitoring data to establish the time series is an important means to predict the development trend of infectious diseases. The established ARIMA model fits well with the predicted value and the predicted value of tsutsugamushi disease in Pinggu, Beijing and can be used as a short-term prognostic tool for tsutsugamushi disease.