白永飞, 徐丽红, 郭支喜, 帖萍, 闫昌福, 赵亮怀. 山西省布鲁氏菌病时间序列自回归移动平均模型分析[J]. 疾病监测, 2011, 26(8): 647-650. DOI: 10.3784/j.issn.1003-9961.2011.08.019
引用本文: 白永飞, 徐丽红, 郭支喜, 帖萍, 闫昌福, 赵亮怀. 山西省布鲁氏菌病时间序列自回归移动平均模型分析[J]. 疾病监测, 2011, 26(8): 647-650. DOI: 10.3784/j.issn.1003-9961.2011.08.019
BAI Yong-fei, XU Li-hong, GUO Zhi-xi, TIE Ping, YAN Chang-fu, ZHAO Liang-huai. Time series Autoregressive Integrated Moving Average model to predict brucellosis incidence in Shanxi province[J]. Disease Surveillance, 2011, 26(8): 647-650. DOI: 10.3784/j.issn.1003-9961.2011.08.019
Citation: BAI Yong-fei, XU Li-hong, GUO Zhi-xi, TIE Ping, YAN Chang-fu, ZHAO Liang-huai. Time series Autoregressive Integrated Moving Average model to predict brucellosis incidence in Shanxi province[J]. Disease Surveillance, 2011, 26(8): 647-650. DOI: 10.3784/j.issn.1003-9961.2011.08.019

山西省布鲁氏菌病时间序列自回归移动平均模型分析

Time series Autoregressive Integrated Moving Average model to predict brucellosis incidence in Shanxi province

  • 摘要: 目的 了解山西省2006 - 2010年布鲁氏菌病(布病)疫情状况,建立布病分析模型,为布病预测与防治提供决策依据。 方法 根据山西省2006 - 2010年布病监测资料,构建时间序列自回归移动平均(ARIMA)模型,并进行了短期预测预报。 结果 所建山西省布病ARIMA模型拟合效果较好,中期布病发病数预测与实际趋势基本一致,结果解释符合专业实际。 结论 ARIMA模型预测山西省布病发病状况可行,结果可信。

     

    Abstract: Objective To establish time series Autoregressive Integrated Moving Average (ARIMA) model to predict brucellosis incidence in Shanxi, and provide evidence for the prevention and control of brucellosis. Methods The time series ARIMA model was established by using brucellosis surveillance data in Shanxi from 2006 to 2010, and short term prediction of brucellosis incidence was made. Results The ARIMA model established fitted well. The medium term predictive incidence was similar to actual one and the result interpretation was reliable. Conclusion It is feasible to use the established ARIMA model in the prediction of the brucellosis incidence in Shanxi.

     

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