Canadian Food Inspection Agency, Epidemiology and Surveillance Section, Atlantic Veterinary College, Department of Health Management, 550 University Avenue, Charlottetown, Prince Edward Island C1A 4P3, Canada.
Prev Vet Med. 2011 May 1;99(2-4):161-75. doi: 10.1016/j.prevetmed.2011.01.005. Epub 2011 Feb 16.
In 2008, Canada designed and implemented the Canadian Notifiable Avian Influenza Surveillance System (CanNAISS) with six surveillance activities in a phased-in approach. CanNAISS was a surveillance system because it had more than one surveillance activity or component in 2008: passive surveillance; pre-slaughter surveillance; and voluntary enhanced notifiable avian influenza surveillance. Our objectives were to give a short overview of two active surveillance components in CanNAISS; describe the CanNAISS scenario tree model and its application to estimation of probability of populations being free of NAI virus infection and sample size determination. Our data from the pre-slaughter surveillance component included diagnostic test results from 6296 serum samples representing 601 commercial chicken and turkey farms collected from 25 August 2008 to 29 January 2009. In addition, we included data from a sub-population of farms with high biosecurity standards: 36,164 samples from 55 farms sampled repeatedly over the 24 months study period from January 2007 to December 2008. All submissions were negative for Notifiable Avian Influenza (NAI) virus infection. We developed the CanNAISS scenario tree model, so that it will estimate the surveillance component sensitivity and the probability of a population being free of NAI at the 0.01 farm-level and 0.3 within-farm-level prevalences. We propose that a general model, such as the CanNAISS scenario tree model, may have a broader application than more detailed models that require disease specific input parameters, such as relative risk estimates.
2008 年，加拿大设计并实施了加拿大可报告性禽流感监测系统（CanNAISS），采用分阶段方法开展了六项监测活动。CanNAISS 是一个监测系统，因为它在 2008 年有超过一项的监测活动或组成部分：被动监测；屠宰前监测；自愿加强可报告性禽流感监测。我们的目标是简要概述 CanNAISS 中的两个主动监测组件；描述 CanNAISS 情景树模型及其在估计群体无 NAI 病毒感染的概率和样本量确定中的应用。我们从屠宰前监测组件中获得的数据包括 2008 年 8 月 25 日至 2009 年 1 月 29 日从 601 个商业鸡和火鸡养殖场采集的 6296 份血清样本的诊断检测结果。此外，我们还包括了来自具有高生物安全标准的农场的子群体的数据：在 24 个月的研究期间（2007 年 1 月至 2008 年 12 月），从 55 个农场重复采样的 36164 个样本。所有提交的样本均未检测到可报告性禽流感（NAI）病毒感染。我们开发了 CanNAISS 情景树模型，以便估计监测组件的敏感性以及在 0.01 农场水平和 0.3 场内水平流行率下群体无 NAI 的概率。我们提出，通用模型（如 CanNAISS 情景树模型）可能比需要疾病特定输入参数（如相对风险估计）的更详细模型具有更广泛的应用。
