| 摘要: |
| 目的 应用乘积季节自回归移动平均（seasonal autoregressive integrated moving average，SARIMA）模型对肺结核发病率进行预测研究，探讨其可行性并为肺结核病的防治工作提供科学依据。方法 应用EViews 7.0.0.1软件对我国2004年1月至2012年12月的肺结核逐月发病率建立乘积SARIMA模型并进行拟合，选取2013年1月至12月肺结核发病率数据评价模型的预测性能。结果 建立的SARIMA（2，0，2）×（0，1，1）12模型能较好地拟合既往时间段内肺结核的发病率，对2013年1月至12月肺结核发病率的预测与实际发病率趋势基本吻合，平均误差绝对值为0.416 992，平均误差绝对率为5.350 8%。结论 乘积SARIMA模型能较好地模拟和预测肺结核发病率在时间序列上的变动趋势，将其应用于肺结核发病预测是可行的，具有推广应用前景。 |
| 关键词: 乘积季节ARIMA模型 肺结核 发病率 预测 |
| DOI：10.16781/j.0258-879x.2016.08.0969 |
| 投稿时间：2016-04-07修订日期：2016-05-23 |
| 基金项目:中国博士后科学基金（2013M542491）. |
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| Multiplicative SARIMA model for prediction of pulmonary tuberculosis incidence |
| HU Xiao-yuan1,SUN Qing-wen2,WANG Ling-ling3,LI Min1* |
(1. Department of Nautlical Injury Protection, Faculty of Naval Medicine, Second Military Medical University, Shanghai 200433, China;
2. Department of Mathematics & Physics, College of Basic Medical Sciences, Second Military Medical University, Shanghai 200433, China;
3. Institute for Tuberculosis Research, No. 309 Hospital of PLA, Beijing 100091, China
*Corresponding author) |
| Abstract: |
| Objective To examine the feasibility of using multiple seasonal autoregressive integrated moving average (SARIMA) model for predicting pulmonary tuberculosis (TB) incidence, so as to provide scientific evidence for the prevention and treatment of TB. Methods EViews 7.0.0.1 software was used to create a SARIMA fit model for seasonal incidence of TB on a monthly basis from January 2004 to December 2012, and the predicting performance of the model was tested with TB data from January to December in 2013. Results The established SARIMA (2,0,2)×(0,1,1)12 model could better fit with the previous TB incidence; and it basically well predicted the TB incidence of the 12 months of 2013, with the mean absolute error being 0.416 992 and the mean absolute error rate being 5.350 8%. Conclusion The established multiplicative SARIMA model can better simulate and predict the trend of TB incidence with time, and it may have a future in predicting the incidence of TB. |
| Key words: multiple seasonal ARIMA model pulmonary tuberculosis incidence forecasting |
