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

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      • 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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