陈小英, 张义, 刘峰, 王林江, 李广智, 陈飒. ARIMA模型在宝鸡市手足口病预警预测中的应用[J]. 疾病监测, 2016, 31(6): 492-497. DOI: 10.3784/j.issn.1003-9961.2016.06.012
引用本文: 陈小英, 张义, 刘峰, 王林江, 李广智, 陈飒. ARIMA模型在宝鸡市手足口病预警预测中的应用[J]. 疾病监测, 2016, 31(6): 492-497. DOI: 10.3784/j.issn.1003-9961.2016.06.012
CHEN Xiao-ying, ZHANG Yi, LIU Feng, WANG Lin-jiang, LI Guang-zhi, CHEN Sa. Application of ARIMA model in prediction of hand foot and mouth disease in Baoji City[J]. Disease Surveillance, 2016, 31(6): 492-497. DOI: 10.3784/j.issn.1003-9961.2016.06.012
Citation: CHEN Xiao-ying, ZHANG Yi, LIU Feng, WANG Lin-jiang, LI Guang-zhi, CHEN Sa. Application of ARIMA model in prediction of hand foot and mouth disease in Baoji City[J]. Disease Surveillance, 2016, 31(6): 492-497. DOI: 10.3784/j.issn.1003-9961.2016.06.012

ARIMA模型在宝鸡市手足口病预警预测中的应用

Application of ARIMA model in prediction of hand foot and mouth disease in Baoji City

  • 摘要: 目的 利用时间序列分析方法动态研究手足口病发病趋势,探讨合理的预测模型,为宝鸡市制定手足口病的预防控制措施提供决策依据。方法 应用时间序列分析方法对宝鸡市2008-2014年手足口病月发病数据进行分析并建立预测模型,对建立的预测模型进行参数估计、模型诊断、模型评价,选择最优预测模型,利用所得到的模型对2015年1-6月的发病情况进行预测,并评价其预测效果。结果 通过参数和模型拟合优度检验以及残差白噪声序列检验,得到模型ARIMA (1,1,1) (0,1,1)12,R2=0.820,标准化的BIC=10.507,Ljung-Box=4.631(P=0.995),2015年1-6月手足口病月发病数预测值和实际值的平均相对误差仅为2.34%,实际值都在95%可信区间内,建立的ARIMA模型的拟合精度和预测效果较为理想。结论 ARIMA模型能较好的模拟宝鸡市手足口病的发病趋势,预测效果可信。

     

    Abstract: Objective To analyze the incidence trends of hand foot and mouth disease (HFMD) dynamically, explore the appropriate HFMD predictive model and provide evidence for the prevention and control of HFMD in Baoji. Methods Time series analysis wasconducted by using the monthly incidence data of HFMD in Baoji from 2008 to 2014, and a predictive model was established after parameter estimation and model evaluation. The model was used to predict the incidence of HFMD during January-June 2015. Results Through the test of parameters and goodness as well as white-noise residuals, we finalized the model ARIMA(1, 1, 1) (0, 1, 1)12, R2=0.820, of which BIC(Bayeian Information Criterion)=10.507, Ljung-Box=4.631, P=0.995.The average relative error between the predictive value and the actual value of the monthly incidence of HFMD during January-June in 2015 was 2.34%.The actual values were within 95%CI of the predictive values. The established ARIMA model was good in fitting precision and prediction effect. Conclusion The model could predict the incidence trend of HFMD for early warning of the disease.

     

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