2009年后我国北方地区流感样病例的动态预警分析

Dynamic surveillance for influenza like illness in northern China after pandemic of influenza A(H1N1) in 2009

目的探索2009年流感大流行后我国北方地区流感样病例(influenza like illness,ILI)预测预警模式,并评价预测预警效果。方法根据2010 2014年我国北方地区ILI哨点监测数据,利用Eviews 6.0软件建立乘积季节ARIMA模型,选择最适宜的时间间隔对2015年1 32周ILI占门急诊病例百分比(ILI%)进行预测。采用流行控制图法探索北方地区ILI预警模型,通过比较灵敏度、特异度和绘制ROC曲线,选择合适的预警界值,结合乘积季节ARIMA模型的预测值,进行预警并评价预警效果。结果建立了乘积季节ARIMA模型(1,0,0)×(1,1,0)52,模型拟合度R2=0.65,不同时间间隔预测结果中,2周时间间隔预测效果较好,且能兼顾预测时效性,模型均方根误差为0.37,平均绝对误差为0.24,平均相对误差百分比为8.26%。ILI%预警界值选用P95预警功效较好,灵敏度为100%,特异度为96%。结合ARIMA模型2周时间间隔预测结果对2015年1 32周北方地区ILI进行预测预警,结果与实报预警一致率为100%。结论利用ARIMA模型和流行控制图结合构建的动态预警模型,能够较好反映我国北方地区ILI流行趋势,为早期发现和控制流感暴发和流行提供依据。

Objective To discuss the dynamic warning model for influenza like illness（ ILI） in northern China after the pandemic of influenza A（ H1N1） in 2009. Methods By using software Eviews 6. 0,seasonal autoregressive integrated moving average（ ARIM A） model was established based on ILI sentinel surveillance data in northern China during 2010-2014,then the optimum time-internal for prediction was selected. Control chart was used to establish a warning model for ILI in northern China. After calculating sensitivity,specificity and describing receiver-operating characteristic curve（ ROC）,the optimal alert threshold was selected. The dynamic warning can be achieved by combining these two models. Results We established the multiple seasonal ARIM A（ 1,0,0） ×（ 1,1,0）52,the R2 value of the model fitting degree was 0. 65. Among these different time-interval patterns,we found 2-week internal had a balance between the effectiveness and the timeliness,the root mean square prediction error was 0. 37,the mean absolute error was 0. 24,and the mean relative error percentage was8. 26%. Selecting P95 as the alert threshold line,the sensitivity was 100% and the specificity was 96%. By using 2-week time-interval pattern to alert ILI% of 1-32 week in northern China in 2015,the predicting result was consistent with actual data. Conclusion We established an early warning model by combining the ARIM A model with control chart,which would reflected the epidemiological trend of ILI cases in northern China and support the early detection and control of influenza outbreak.