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江苏省乙型肝炎流行趋势的时间序列分析及预测
引用本文:吴莹,刘文东,梁祁,胡建利,艾静,李媛. 江苏省乙型肝炎流行趋势的时间序列分析及预测[J]. 江苏预防医学, 2010, 21(6): 15-17. DOI: 10.3969/j.issn.1006-9070.2010.06.006
作者姓名:吴莹  刘文东  梁祁  胡建利  艾静  李媛
作者单位:江苏省疾病预防控制中心,江苏,南京,210009
摘    要:
目的:利用拟合ARIMA模型对江苏省乙肝发病趋势进行时间序列分析和预测,为制定乙肝防治策略提供科学依据。方法:收集江苏省2000年~2009年乙肝月发病率资料,通过SPSS13.0软件拟合ARIMA模型。结果:最终拟合为ARIMA(0,1,2)(0,1,1)12模型,残差为白噪声序列,预测值与实际值的平均相对误差为8.26%。结论:江苏省乙肝发病具有逐渐下降的长期趋势,说明乙肝防治卓有成效。2000年以来乙肝月发病率时间序列还有明显的周期性波动特征,其原因有待于进一步研究。2010年预测结果提示需调整本省乙肝防治策略。

关 键 词:乙型肝炎  时间序列  ARIMA模型  预测

Time series analyzing and forecasting to epidemic tendency of hepatitis B in Jiangsu province
Wu Ying,Liu Wen-dong,Liang Qi,Hu Jian-li,Ai Jing,Li Yuan. Time series analyzing and forecasting to epidemic tendency of hepatitis B in Jiangsu province[J]. Jiangsu Journal of Preventive Medicine, 2010, 21(6): 15-17. DOI: 10.3969/j.issn.1006-9070.2010.06.006
Authors:Wu Ying  Liu Wen-dong  Liang Qi  Hu Jian-li  Ai Jing  Li Yuan
Affiliation:(Jiangsu Province Center for Disease Prevention and Control,Nanjing 210009,China)
Abstract:
Objective:To analyze and forecast incidence of hepatitis B in Jiangsu province with a auto regressive integrated moving average(ARIMA)model,which may provide a scientific basis for developing prevention and control strategies on hepatitis B in the future.Methods:Incidence of hepatitis B was collected monthly from 2000 to 2009 and a model(ARIMA)was fit with SPSS 13.0 software.Rerults:The model ARIMA(0,1,2)(0,1,1)12 was established finally and the residual sequence was a white noise sequence.The relative error in average was 8.26% between the forecasting value and the real value.Conclusion:The incidence of hepatitis B had a long-term descending trend in the last 10 years in Jiangsu province,which declared that the prevention and control on hepatitis B had achieved remarkable success in this area.The incidence sequence of hepatitis B has fluctuated periodically since 2000,and the reason for this is unclear.The predicted values in 2010 suggested that the prevention and control strategies on hepatitis B should be explored further.
Keywords:hepatitis B time series ARIMA forecast
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