论文部分内容阅读
目的探讨自回归求和移动平均模型(Auto Regressive Integrated Moving Average,ARIMA)在江西省肾综合征出血热月发病率预测的可行性,为制定出血热防控策略提供依据。方法基于江西省2006-2015年肾综合征出血热(Hemorrhagic Fever with Renal Syndrome,HFRS)逐月发病率资料建立ARIMA模型,利用2016年各月发病率检验模型预测效果,再以2006-2016年HFRS逐月发病率构建模型预测2017年HFRS发病率。结果本研究构建的ARIMA(0,0,2)(0,1,1)12模型,拟合结果与实际发病情况基本吻合。各项参数均有统计学意义(P<0.05),BIC值(Schwarz Bayesian criterion,贝叶斯信息准则)=-6.792,Ljung-Box Q=14.992,P=0.452,模型残差为白噪声;2016年各月HFRS发病率预测值与实际值动态趋势基本吻合。预测2017年江西省HFRS发病率为1.45/10万。结论 ARIMA模型能很好地模拟江西省HFRS发病率在时间序列上的变动趋势,可用于江西省HFRS发病率的短期预测研究。
Objective To investigate the feasibility of predicting monthly morbidity of hemorrhagic fever with renal syndrome in Jiangxi Province by using Auto Regressive Integrated Moving Average (ARIMA) model and to provide a basis for the development of hemorrhagic fever prevention and control strategies. Methods Based on the monthly incidence of Hemorrhagic Fever with Renal Syndrome (HFRS) in Jiangxi Province from 2006 to 2015, the ARIMA model was established. By using the monthly morbidity test model in 2016 to predict the effect, Monthly morbidity build models predict the incidence of HFRS in 2017. Results The ARIMA (0,0,2) (0,1,1) 12 model constructed in this study is basically consistent with the actual incidence. (P <0.05). The Schwarz Bayesian criterion (BIC) = 6.792, Ljung-Box Q = 14.992, P = 0.452, and the model residual was white noise. The annual incidence of HFRS predicted value and the actual value of the dynamic trend of the basic agreement. The incidence of HFRS in Jiangxi Province in 2017 is estimated to be 1.45 / 100,000. Conclusion The ARIMA model can well simulate the time series trend of HFRS incidence in Jiangxi Province and can be used for short-term prediction of the incidence of HFRS in Jiangxi Province.