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中国防痨杂志 ›› 2012, Vol. 34 ›› Issue (7): 459-462.

• 论著 • 上一篇    下一篇

新陈代谢灰色模型对广东省肺结核报告发病率的预测分析

蒋莉 钟球 周琳 李建伟 连永娥   

  1. 510630  广州,广东省结核病控制中心
  • 收稿日期:2012-05-16 出版日期:2012-07-10 发布日期:2012-07-06
  • 通信作者: 钟球 E-mail:gdtb@vip.163.com
  • 基金资助:

    国家“十二五”科技重大专项(2012ZX10004903);广东省医学科学技术研究基金(C2010005)

Application of the grey model of metabolism for prediction of the pulmonary tuberculosis incidence in Guangdong province

JIANG Li, ZHONG Qiu, ZHOU Lin, LI Jian-wei, LIAN Yong-e   

  1. Center for Tuberculosis Control of Guangdong Province, Guangzhou 510630, China
  • Received:2012-05-16 Online:2012-07-10 Published:2012-07-06
  • Contact: ZHONG Qiu E-mail:gdtb@vip.163.com

摘要: 目的  利用新陈代谢灰色模型预测广东省结核病发病趋势,为制定结核病的防控措施提供依据。 方法  根据广东省2001—2011年肺结核报告发病率数据分别建立常规灰色模型[grey model(1,1),简称GM(1,1)]和新陈代谢灰色模型[简称新陈代谢GM(1,1)模型]。通过短序列与长序列预测结果,比较两种预测精度和准确性,选择最优的模型进行外推预测。模型的预测精度以后验差比值C和小误差概率P表示,精度等级越小越好,精度一级最好,四级不合格。 结果  不同维度的新陈代谢GM(1,1)模型的误差小于相应维度的常规GM(1,1)模型,短序列预测的误差小于长序列。5维新陈代谢GM(1,1) 模型精度检验指标均为一级,后验差比值C和小误差概率P分别为0.14和1。 结论  新陈代谢GM(1,1)模型是处理此类数据较为理想的模型,5维新陈代谢GM(1,1)模型对于广东省肺结核报告发病率的预测具有一定优势。

关键词: 结核,肺/流行病学, 发病率, 预测, 模型,统计学, 结核, 肺/预防和控制, 广东省

Abstract: Objective  To utilize the metabolizing grey model to predict the trend of TB incidence and provide scientific evidence for formulating the related measures of prevention and control.  Methods  According to the incidence of pulmonary TB in the Guangdong province from 2001—2011, we established the conventional grey model [gray model (1,1), referred to as GM (1,1)] and the grey model of metabolism [referred to as the metabolic GM (1,1) model]. We compared the precision and accuracy of model predictions by the results of short series and long series, then chose the best model for extrapolation forecast. The prediction accuracy can be showed by posterior error ratio C and small probability of error P. The smaller class the best accuracy, fist class indicate the best and four failed.  Results  The error of the grey model of metabolism is less in comparison with the corresponding dimensions of conventional grey model. The error is less in the shorter series prediction. The result of accuracy test showed that the 5-dimensional grey model of metabolism is in the first class. The model of posterior error ratio and small probability of error was 0.14 and 1 respectively.  Conclusion  The metabolic GM (1,1) is the ideal model to deal with such data, the five-dimensional metabolic GM (1,1) model has certain advantages to forecast the TB incidence in Guangdong province.

Key words: Tuberculosis,pulmonary/epidemiology, Incidence, Forecasting, Models,statistical, Tuberculosis,pulmonary/prevention &, control, Guangdong province