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张静, 刘志东, 劳家辉, 刘言玉, 姜宝法. 基于时间序列分解法预测肾综合征出血热发病趋势和季节性[J]. 中国公共卫生, 2018, 34(7): 1037-1040. DOI: 10.11847/zgggws1116074
引用本文: 张静, 刘志东, 劳家辉, 刘言玉, 姜宝法. 基于时间序列分解法预测肾综合征出血热发病趋势和季节性[J]. 中国公共卫生, 2018, 34(7): 1037-1040. DOI: 10.11847/zgggws1116074
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Jing ZHANG, Zhi-dong LIU, Jia-hui LAO, . Prediction for trend and seasonal variation of incidence of hemorrhagic fever with renal syndrome: time series decomposition analysis[J]. Chinese Journal of Public Health, 2018, 34(7): 1037-1040. DOI: 10.11847/zgggws1116074
Citation: Jing ZHANG, Zhi-dong LIU, Jia-hui LAO, . Prediction for trend and seasonal variation of incidence of hemorrhagic fever with renal syndrome: time series decomposition analysis[J]. Chinese Journal of Public Health, 2018, 34(7): 1037-1040. DOI: 10.11847/zgggws1116074
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基于时间序列分解法预测肾综合征出血热发病趋势和季节性

Prediction for trend and seasonal variation of incidence of hemorrhagic fever with renal syndrome: time series decomposition analysis

    摘要
  • 摘要:
      目的  基于时间序列分解法研究中国2011 — 2016年肾综合征出血热（HFRS）的发病趋势和季节性，建立预测模型并评价效果。
      方法  应用时间序列分解法分解中国2011 — 2016年HFRS的发病趋势和季节性，以剔除季节变动因素（S2）的非季节性数据建模，再乘以S2为最终预测模型，回代检验评价其预测精度。
      结果  中国2011 — 2016年HFRS的发病趋势为先上升后下降，季节性明显；发病高峰呈双峰型，以5 — 6月和11月 — 次年1月为发病高峰。建立ARIMA （2，1，1）模型，模型AIC = 866.4，各项参数（AR1 = 0.786 7，AR2 = – 0.354 3，MA = – 0.744 1）均有统计学意义（均P < 0.01）, 残差为白噪声序列Q（20） = 16.364，P = 0.694。月发病数的预测公式为 Yi =ARIMA （2，1，1）× S2 ，中国2011 — 2016年HFRS月发病数回代检验的平均绝对误差（MAE）为71.31，平均绝对百分误差（MAPE）为7.00 %。
      结论  时间序列分解法可用来预测HFRS的发病趋势和季节性，以剔除季节变动因素的数据建立HFRS的月发病数预测模型是可行的。

     

    Abstract:
      Objective  To establish a predictive model for incidence trend and seasonality of hemorrhagic fever with renal syndrome (HFRS) in China and to evaluate the efficacy of the model based on time series decomposition analysis on the data between 2011 – 2016.
      Methods  Data on reported monthly HFRS incidences during 2011 – 2016 across China were collected via the website of National Health and Family Planning Commission of People’s Republic of China. We analyzed incidence trend and seasonality of HRFS during the period using time series decomposition. We performed modeling using the data with seasonal variable (S2) having been removed; then the constructed model was multiplied by S2 to establish a final model. The prediction accuracy was evaluated using back-substitution method.
      Results  The incidence of HFRS rose first and then declined, with an obvious seasonality, during the 6-year period in China. The HFRS incidence showed a bimodal distribution during a 12-month period, with the first peak from May to June of a year and the second from November of a year to January of the next year. The parameters of the established autoregressive integrated moving average (ARIMA) (2, 1, 1) were as following: Akaike information criterion = 866.4, autoregression at-lag-1 (AR1) = – 0.3543, AR2 = – 0.3543, moving average = – 0.7441 (P < 0.01); the residual of the established model was a white noise sequence (Q(20) = 16.364, P = 0.694). The formula for prediction of monthly number of HFRS was S2 × ARIMA (2, 1, 1). The results of back-substitution revealed a mean absolute error (MAE) of 71.31 and an average absolute percentage error (MAPE) of 7.00% for predicted monthly incidence number of HFRS during the 6-year period across China.
      Conclusion  Time series decomposition can be used to predict the trend and seasonal variation HFRS incidence and applicable model for the prediction of monthly HFRS incidence could be established using data with seasonal variable (S2) having been removed.

     

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