CAJ | 학술논문

目的:探讨应用时间序列SARIMA模型进行肾综合征出血热发病率预测的可行性.方法:首先利用余弦函数模型分析肾综合征出血热季节性发病规律,其次进行扩充迪基富勒的平稳性单位根检验.然后根据自相关函数和偏自相关函数判别月别疫情间的相关性.最后基于1990年-2004年逐月发病率进行SARIMA模型建模拟合,利用2005年各月发病率进行外推预测,并与实际值进行比较.上述统计分析采用Eviews3.1和SPSS12.0软件完成.结果:余弦函数确定的高峰时点为3月中旬.高峰时区为3月1日到4月3日.含第一谐量的余弦方程为:(Y)_(1i)=1.274-0.945cos(t_i-76.684),决定系数R~2=0.853;在备选模型中,SARIMA (1,0,0)×(2,0,0)_(12)模型不仅很好地拟合了既往时间段上的发病率序列,而且对2005年各月发病率的预测值符合实际发病率变动趋势.结论:余弦函数对于褐家鼠型肾综合征出血热疫情季节分布拟合较好,SARIMA模型能很好地模拟传染病发病率在时间序列上的变动趋势,并对未来的发病率进行预测,为传染病防制工作服务.
목적:탐토응용시간서렬SARIMA모형진행신종합정출혈열발병솔예측적가행성.방법:수선이용여현함수모형분석신종합정출혈열계절성발병규률,기차진행확충적기부륵적평은성단위근검험.연후근거자상관함수화편자상관함수판별월별역정간적상관성.최후기우1990년-2004년축월발병솔진행SARIMA모형건모의합,이용2005년각월발병솔진행외추예측,병여실제치진행비교.상술통계분석채용Eviews3.1화SPSS12.0연건완성.결과:여현함수학정적고봉시점위3월중순.고봉시구위3월1일도4월3일.함제일해량적여현방정위:(Y)_(1i)=1.274-0.945cos(t_i-76.684),결정계수R~2=0.853;재비선모형중,SARIMA (1,0,0)×(2,0,0)_(12)모형불부흔호지의합료기왕시간단상적발병솔서렬,이차대2005년각월발병솔적예측치부합실제발병솔변동추세.결론:여현함수대우갈가서형신종합정출혈열역정계절분포의합교호,SARIMA모형능흔호지모의전염병발병솔재시간서렬상적변동추세,병대미래적발병솔진행예측,위전염병방제공작복무.
Objective:To explore the feasibility of Seasonal Autoregressive Integrated Moving Average(SARIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) in Huludao City. Methods: Firstly, cosine function models were use to analyze the seasonal rule of HFRS. Secondly, Augmented Dickey Fuller (ADF) unit root test was applied for series trend analysis. Thirdly, Autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to detect autocorrelations of the HFRS incidence. Finally, EViews3.1 and SPSS 12. 0 software were performed to construct the ARIMA model based on the month incidence of contagious disease in Huludao City from January 1990 to December 2004. Then the constructed model was used to predict the month incidence in 2005 with a comparison to actual value. Results: The cosine function showed that the high incidence rate was in the middle of March (March 1 to April 3). Single cosine function equation was (Y)_(1i)=1. 274--0. 945cos(t_i-76. 684) with determinant coefficient R~2 equal to 0. 853. SARIMA (1,0, 0) × (2,0,0)_(12) exactly fitted the incidence of the previous month. Incidence in 2005 by the model was consistent with the actual incidence. Conclusion: The method of time series analysis included cosine function and SARIMA can be used to fit exactly the changes of the incidence of HFRS and to predict the incidence in future.