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目的监测和分析无锡市传染性非典型肺炎(SARS)预警数据及流感样病例2个发热呼吸道症状监测系统的流行病学特征和病原学特征,建立无锡市发热呼吸道监测预警限值,评价监测系统在流感暴发流行中的早期预警效果。方法对2005—2010年无锡市SARS预警数据及流感样病例监测数据进行分析,采用指数加权移动平均(EWMA)法建立预警上限值,分析其对流感暴发流行早期预警的效果。结果流感样病例(ILI)监测数据和SARS预警监测数据即急性(发热)呼吸道疾病(ARI)显示2个监测数据均有3个明显的高峰,分别是2006年第11～14周、2009年第33～36周和2009年第46～48周,两条移动平均线趋势基本一致;病原学监测显示每年的流感病毒分离阳性率高峰与ILI和ARI高峰期相吻合,2006年主要是B型的暴发流行和A(H1N1)亚型的散发为主,而2009年8、9月份出现4种毒株共同检出的现象,但逐渐被新出现的甲型H1N1毒株代替。EWMA预警结果显示,ARI和ILI 2种监测数据在2005年第47周、2006年第11～13周、2009年第11周、33～34周、36周、46周均出现预警,这与无锡市2005—2010年期间流感暴发疫情监测情况一致。结论 2个监测系统同时出现的预警信号和实际流感暴发疫情基本吻合,利用EWMA法对多种监测数据进行预警分析比对单种监测数据进行预警更加科学和可靠,在流行时间上更为准确。
Objective To monitor and analyze the epidemiological and etiological characteristics of two surveillance systems of respiratory symptoms of SARS in Wuxi early warning data and influenza-like illness cases and to establish the early warning limits of fever and respiratory tract in Wuxi City. The evaluation and monitoring system Early warning in the flu outbreak. Methods The surveillance data of SARS early warning and flu-like cases in Wuxi from 2005 to 2010 were analyzed. The upper limit of early warning was established by exponential weighted moving average (EWMA) method, and the effect of early warning on the outbreak of influenza was analyzed. Results The monitoring data of influenza-like illness (ILI) and SARS surveillance data (acute respiratory syndrome (ARI)) showed that there were three obvious peaks in the two monitoring data, respectively from the 11th to the 14th in 2006, 33 to 36 weeks and 46 to 48 weeks in 2009, the trend of the two moving average lines are basically the same; etiological monitoring showed that the annual peak of the positive rate of influenza virus isolation coincided with the peak of ILI and ARI, mainly in 2006, type B Outbreaks and distribution of A (H1N1) subtype. However, in August and September 2009, four strains were commonly detected but gradually replaced by the newly emerged A (H1N1) strains. EWMA early warning results showed that the monitoring data of ARI and ILI showed an early warning in the 47th week of 2005, the 11th week of 2006, the 11th week of 2009, the 33rd week of 34th week, the 36th week of 46th week and the 46th week of 2009, City during the period 2005-2010 influenza outbreak surveillance consistent. Conclusions The early warning signals of the two monitoring systems coincide with the outbreaks of actual influenza. It is more scientific and reliable to forecast early warning of multiple monitoring data by using EWMA method and more accurate in epidemic time.