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    李盛, 王宇红, 王金玉, 冯亚莉, 李普, 董继元, 马汉平, 王龄庆, 常旭红, 李守禹, 张薇, 张晓宇, 贾清, 张艳. 应用自回归移动平均模型乘积季节模型预测兰州市水相关疾病发病情况[J]. 环境卫生学杂志, 2019, 9(2): 134-138. DOI: 10.13421/j.cnki.hjwsxzz.2019.02.008
    引用本文: 李盛, 王宇红, 王金玉, 冯亚莉, 李普, 董继元, 马汉平, 王龄庆, 常旭红, 李守禹, 张薇, 张晓宇, 贾清, 张艳. 应用自回归移动平均模型乘积季节模型预测兰州市水相关疾病发病情况[J]. 环境卫生学杂志, 2019, 9(2): 134-138. DOI: 10.13421/j.cnki.hjwsxzz.2019.02.008
    shu
    LI Sheng, WANG Yuhong, WANG Jinyu, FENG Yali, LI Pu, DONG Jiyuan, MA Hanping, WANG Linqing, CHANG Xuhong, LI Shouyu, ZHANG Wei, ZHANG Xiaoyu, JIA Qing, ZHANG Yan. Prediction of the Incidence of Water-borne Diseases in Lanzhou by Multiplicative Seasonal ARIMA Model[J]. Journal of Environmental Hygiene, 2019, 9(2): 134-138. DOI: 10.13421/j.cnki.hjwsxzz.2019.02.008
    Citation: LI Sheng, WANG Yuhong, WANG Jinyu, FENG Yali, LI Pu, DONG Jiyuan, MA Hanping, WANG Linqing, CHANG Xuhong, LI Shouyu, ZHANG Wei, ZHANG Xiaoyu, JIA Qing, ZHANG Yan. Prediction of the Incidence of Water-borne Diseases in Lanzhou by Multiplicative Seasonal ARIMA Model[J]. Journal of Environmental Hygiene, 2019, 9(2): 134-138. DOI: 10.13421/j.cnki.hjwsxzz.2019.02.008
    shu

    应用自回归移动平均模型乘积季节模型预测兰州市水相关疾病发病情况

    Prediction of the Incidence of Water-borne Diseases in Lanzhou by Multiplicative Seasonal ARIMA Model

      摘要
    • 摘要:
      目的 探讨自回归移动平均模型（autoregressive integrated moving average model，ARIMA）乘积季节模型在水相关疾病发病率发病趋势预测中的应用，对兰州市水相关疾病发病情况进行预测。
      方法 收集2006年1月—2014年12月水相关疾病发病率数据，利用R软件构建ARIMA乘积季节模型，利用2015—2017年实际发病率与模型拟合数据比较，评价模型的预测性能，并预测2015—2017年水相关疾病的发病率。
      结果 在水相关疾病预测中建立ARIMA（2，0，1）×（2，0，0）12乘积季节模型，Ljung-Box检验差异无统计学意义（Q=18.64，P=0.824），2015年—2017年兰州市常见水相关疾病实际发病率均在预测结果95%可信区间内，平均预测相对误差为5%。
      结论 ARIMA乘积季节模型可以较好的预测兰州市水相关疾病发病率的变化趋势，能够运用于水相关疾病发病趋势的预测及预警，为防控措施的制定提供参考。

       

      Abstract:
      Objectives To explore the feasibility of using a multiplicative seasonal autoregressive integrated moving average (ARIMA) model in predicting the incidence trend of water-borne diseases, and forecasting the incidence of water borne diseases in Lanzhou.
      Methods The incidence data of water-borne diseases from January, 2006 to December, 2014 in Lanzhou was collected and R software was utilized to establish the multiplicative seasonal ARIMA model. Compare actual incidence during 2015—2017 with data fitted by the model in order to evaluate the prediction performance of the model and predict the incidence of water-borne diseases during 2015—2017.
      Results A multiplicative seasonal ARIMA (2, 0, 1)×(2, 0, 0)12 model was established. There was no statistically significant difference (Q=16.14, P=0.707) tested by the Ljung—Box test, all actual values of the incidence of water-borne diseases from 2015 to 2017 were in the 95% confidence interval of predicted values, the mean relative error was 5%.
      Conclusions Multiplicative seasonal ARIMA model was suitable for predicting the variation trend of the incidence of water-borne diseases in Lanzhou and could be applied for prediction and early warning of the incidence trend of water borne diseases, which provides reference for the formulation of prevention and control measures.

       

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