文章摘要
张文娟,刘文东,胡建利,汤奋扬,彭志行,喻荣彬.基于ARIMA模型的江苏省梅毒疫情预测[J].南京医科大学学报,2017,(5):649~652
基于ARIMA模型的江苏省梅毒疫情预测
Forecast of syphilis epidemic situation in Jiangsu Province base on ARIMA model
投稿时间:2016-11-13  
DOI:10.7655/NYDXBNS20170531
中文关键词: ARIMA模型  时间序列分析  梅毒  预测
英文关键词: ARIMA model  time series analysis  syphilis  predict
基金项目:国家自然科学基金(81673275,U1503123);国家“十二五”重大科技专项(2012ZX10001-001);江苏省高校优势学科建设工程资助
作者单位
张文娟 南京医科大学公共卫生学院流行病与卫生统计学系江苏 南京 211166 
刘文东 江苏省疾病预防控制中心江苏 南京 210009 
胡建利 江苏省疾病预防控制中心江苏 南京 210009 
汤奋扬 江苏省疾病预防控制中心江苏 南京 210009 
彭志行 南京医科大学公共卫生学院流行病与卫生统计学系江苏 南京 211166 
喻荣彬 南京医科大学公共卫生学院流行病与卫生统计学系江苏 南京 211166 
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中文摘要:
      目的:了解江苏省梅毒的流行病学特点,构建预测江苏省梅毒月发病率的自回归移动平均模型(autoregressive integrated moving average model,ARIMA),为梅毒预防控制工作提供参考依据。方法:利用江苏省1995—2009年梅毒月发病率资料建立ARIMA预测模型,并进行模型评价。结果:拟合ARIMA(1,1,0),(2,1,0)模型为预测江苏省梅毒月发病率的最佳模型,模型周期性、季节性一阶、二阶系数分别为-0.579、-0.245、-0.357,t检验统计量分别为8.777,2.881,4.766,相应的P值分别为<0.001、0.005、<0.001,表明该模型具有较高的预测精度,预测值与实际值较为接近,且实际值均在预测值的95%置信区间范围内,预测效果较好。结论:ARIMA模型能较好地预测梅毒发病率的变化趋势,为梅毒预防控制措施的制定提供重要依据。
英文摘要:
      Objective:To investigate the epidemiologic characteristics of syphilis,establish an Autoregressive Integrated Moving Average Model(ARIMA) model for the prediction of monthly incidence of syphilis in Jiangsu and provide evidence for the prevention and control of the disease. Methods:We used monthly incidence data of syphilis in Jiangsu from 1995 to 2009 to establish the ARIMA model,and then evaluated the model. Results:ARIMA(1,1,0),(2,1,0) models are the optimal models to predict the monthly incidence of syphilis in Jiangsu,the coefficients of recurrent model,seasonal first-order model,seasonal second-order model respectively are -0.579,-0.245,-0.357,statistics of t test respectively are 8.777,2.881,4.766. Correspondingly,the values of P respectively are 0.000,0.005,<0.001. The model had favorably high precision,the predicting value was close to the true value,which was with in the 95% confidence interval of the predicting value. Conclusion:The ARIMA model was suitable to forecast the incidence of syphilis.ARIMA model could be used to predict the incidence trend of syphilis and provide evidence for the development of syphilis prevention and control measures.
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