文章摘要
孟蕾,王新华,李娟生,任晓卫,李红育,胡晓斌,杨筱婷,秦林原,陈建华,白亚娜.甘肃省2006-2011年哨点监测流感样病例动态预警分析[J].中华流行病学杂志,2012,33(11):1155-1158
甘肃省2006-2011年哨点监测流感样病例动态预警分析
Dynamic prediction on the number of influenza-like cases in Gansu province based on data from the influenza sentinel surveillance program, from 2006 to 2011
收稿日期:2012-07-10  出版日期:2014-09-03
DOI:10.3760/cma.j.issn.0254-6450.2012.11.013
中文关键词: 流感  预警  哨点监测
英文关键词: Influenza  Early warning  Sentinel surveillance
基金项目:国家科技重大专项(2009ZXl0004—208)
作者单位E-mail
孟蕾 甘肃省疾病预防控制中心, 兰州 730000  
王新华 甘肃省疾病预防控制中心, 兰州 730000  
李娟生 兰州大学公共卫生学院  
任晓卫 兰州大学公共卫生学院  
李红育 甘肃省疾病预防控制中心, 兰州 730000  
胡晓斌 兰州大学公共卫生学院  
杨筱婷 甘肃省疾病预防控制中心, 兰州 730000  
秦林原 兰州大学公共卫生学院  
陈建华 甘肃省疾病预防控制中心, 兰州 730000  
白亚娜 兰州大学公共卫生学院 baiyana@lzu.edu.Cn 
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中文摘要:
      目的 掌握甘肃省流感样病例发生动态并探索其预警模式和效果.方法 通过甘肃省2006-2011年哨点流感样病例监测数据,应用时序图分析其变动趋势,并采用流行控制图-移动百分位数法探索流感样病例预警模式,结合统计模型的预测值,评价预警效果.结果 甘肃省2006和2009年为流感高流行期,2007和2008年为流感低流行期.流感样病例流行控制预警线显示冬季预警值较高,夏季较低的特征.应用简单季节性指数平滑模型和自回归移动平均模型(ARIMA)(1,1,1)(0,1,0)乘积季节性模型动态预测2011年每周流感样病例占门急诊病例百分比(ILI%),二者动态预测预警与实报预警一致率均为100%.但从预测的均方根误差分析,指数平滑模型动态预测效果优于ARIMA模型.结论 流感动态预警模式可反映甘肃省流感样病例的流行规律,但预警技术存在局限性.
英文摘要:
      Objective To understand the epidemiological trend on the number of influenzalike cases and to explore the feasibility of early warning systems of influenza in Gansu province. Methods Based on data from the influenza sentinel surveillance program, a sequence chart was used to analyze the epidemiological trend on the number of influenza-like illness (ILI) cases. Both control chart and mobile percentile method were used to select the threshold of premium alert for the ILI of sentinel surveillance program. Warning effects were assessed by statistical model. Results The prevalence of influenza were both low in 2007 and 2008. Alert thresholds for ILI of Sentinel surveillance was built. The thresholds were higher alert in winter, but lower in summer. Both Seasonal Exponential Smoothing Model and Multiplicative Seasonal ARMA Model (1, 1, 1) (0, 1, 0) were used to dynamically predict the weekly percentage of outpatient visits for influenza-like illness (ILI%)of 2011. The concordance rates (predicted=actual) were 100% for both of them. According to the RMSE values, the dynamically predicted effect of the seasonal exponential smoothing model was superior to ARIMA. Conclusion Dynamic prediction on the number of influenza-like cases could reflect the epidemiological trend of influenza in Gansu province, but with some limitations.
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