应用SARIMA模型预测肺结核发病趋势
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摘要
目的用SARIMA模型预测云南省肺结核发病趋势。方法收集云南省16个州市2005年1月至2017年12月肺结核月发病数据资料,应用SPSS20.0软件构建模型,预测云南省2018年1月至12月的发病数和各州市发病趋势。结果 SARIMA(1,1,1)×(0,1,0)_(12)模型较好地拟合了云南省肺结核的月发病数据,各州市模型预测2018年10个州市肺结核发病数呈现上升趋势。结论 SARIMA模型能很好地模拟肺结核发病数在时间序列轴上的变化趋势,将其应用于未来发病数的短期预测,可为肺结核的防控提供参考。
Objective To predict the incidence of tuberculosis in Yunnan Province with a seasonal ARIMA model.Methods Time series data of monthly tuberculosis cases from Jan 2005 to Dec 2017 in Yunnan Province and 16 prefecture-level cities were analyzed using seasonal ARIMA model,and a predictive model was established to predict the incidence from January to December in 2018.Results Model of ARIMA(1,1,1)×(0,1,0)12 exactly fitted the incidence in the previous months.Eventually,the optimal model of the 16 prefecture-level cities was established,and the monthly tuberculosis cases from Jan to Dec 2018 predicted by the optimal model were improved in Puer and Xishuangbanna Prefecture-level cities.Conclusion Seasonal ARIMA model can be used to fit the dynamic changes of tuberculosis incidence and to predict the incidence in the future,so as to provide scientific guidance for its prevention and control.
Objective To predict the incidence of tuberculosis in Yunnan Province with a seasonal ARIMA model.Methods Time series data of monthly tuberculosis cases from Jan 2005 to Dec 2017 in Yunnan Province and 16 prefecture-level cities were analyzed using seasonal ARIMA model,and a predictive model was established to predict the incidence from January to December in 2018.Results Model of ARIMA(1,1,1)×(0,1,0)12 exactly fitted the incidence in the previous months.Eventually,the optimal model of the 16 prefecture-level cities was established,and the monthly tuberculosis cases from Jan to Dec 2018 predicted by the optimal model were improved in Puer and Xishuangbanna Prefecture-level cities.Conclusion Seasonal ARIMA model can be used to fit the dynamic changes of tuberculosis incidence and to predict the incidence in the future,so as to provide scientific guidance for its prevention and control.
引文
[1] 陆伟,虞浩,陈诚,等.县(区)级结核病防治定点医院模式的实施效果分析[J].中国防痨杂志,2013,35(10):783-787.
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[3] 张彦琦,唐贵立,王文昌,等.ARIMA模型及其在肺结核预测中的应用[J].现代预防医学,2008,35(9):1608-1610,1615.
[4] 孟蕾,王玉明.ARIMA模型在肺结核发病预测中的应用[J].中国卫生统计,2010,27(5):507-509.
[5] 李娜,殷菲,李晓松.时间序列分析在结核病发病预测应用中的初步探讨[J].现代预防医学,2010,37(8):1426-1428.
[6] 宇传华.SPSS与统计分析[M].北京:电子工业出版社,2007.
[7] 刘刚,单芙香.ARIMA模型及其在麻疹发病率预测中的应用[J].数理医药学杂志,2011,24(4):379-382.
[8] 刘涛,王显军,姜宝法,等.SARIMA模型预测山东省手足口病发病趋势[J].中国卫生统计,2013,30(5):697-700.
[9] 王涛,苑新海,朱宗龙.ARIMA时间序列在乙肝发病预测中的应用[J].中国卫生统计,2014,31(4):646-647,650.
[10] 胡建利,梁祁,吴莹,等.季节时间序列模型在菌痢发病预测中的应用[J].中国卫生统计,2012,29(1):34-36,39.
[11] 李润滋,章涛,梁玉民,等.SARIMA模型在流行性腮腺炎发病预测中的应用[J].山东大学学报(医学版),2016,54(9):82-86,96.
[2] Huang L,Li XX,Abe EM,et al.Spatial-temporal analysis of pulmonary tuberculosis in the Northeast of the Yunnan province,People’s Republic of China[J].Infectious Diseases of Poverty,2017,6(2):57-67.
[3] 张彦琦,唐贵立,王文昌,等.ARIMA模型及其在肺结核预测中的应用[J].现代预防医学,2008,35(9):1608-1610,1615.
[4] 孟蕾,王玉明.ARIMA模型在肺结核发病预测中的应用[J].中国卫生统计,2010,27(5):507-509.
[5] 李娜,殷菲,李晓松.时间序列分析在结核病发病预测应用中的初步探讨[J].现代预防医学,2010,37(8):1426-1428.
[6] 宇传华.SPSS与统计分析[M].北京:电子工业出版社,2007.
[7] 刘刚,单芙香.ARIMA模型及其在麻疹发病率预测中的应用[J].数理医药学杂志,2011,24(4):379-382.
[8] 刘涛,王显军,姜宝法,等.SARIMA模型预测山东省手足口病发病趋势[J].中国卫生统计,2013,30(5):697-700.
[9] 王涛,苑新海,朱宗龙.ARIMA时间序列在乙肝发病预测中的应用[J].中国卫生统计,2014,31(4):646-647,650.
[10] 胡建利,梁祁,吴莹,等.季节时间序列模型在菌痢发病预测中的应用[J].中国卫生统计,2012,29(1):34-36,39.
[11] 李润滋,章涛,梁玉民,等.SARIMA模型在流行性腮腺炎发病预测中的应用[J].山东大学学报(医学版),2016,54(9):82-86,96.