Adapting a scenario tree model for freedom from disease as surveillance progresses: The Canadian notifiable avian influenza model

Scenario tree models with temporal discounting have been applied in four continents to support claims of freedom from animal disease. Recently, a second (new) model was developed for the same population and disease. This is a natural development because surveillance is a dynamic process that needs to adapt to changing circumstances – the difficulty is the justification for, documentation of, presentation of and the acceptance of the changes.     

基于最大熵模型对蓝舌病病毒传播媒介库蠓在中国分布特征的研究

蓝舌病是由蓝舌病毒(BTV)引起的一种非接触性传染病,主要侵害反刍动物,库蠓是蓝舌病毒传播最重要的媒介生物。本研究基于尖喙库蠓地理分布数据及环境数据,利用最大熵生态位模型(MaxEnt)对尖喙库蠓在中国大陆的分布情况进行了预测,并评估了环境变量对尖喙库蠓分布的影响。结果显示,最适合尖喙库蠓生存的地区主要分布在中国的西北和东北部分地区,以及南方的大部分地区;Jackknife分析结果显示,土壤有效水含量(25%)、最冷月最低温(18.1%)、最干月降雨量(18%)、平均最大风速(13.2%)是影响尖喙库蠓分布最主要的4个环境因子,其中最干月降雨量是模型中影响尖喙库蠓分布的最重要环境变量。本研究首次将这种生态位模型用于预测中国库蠓的分布,为蓝舌病的预防控制工作提供参考依据,同时也为库蠓监测管理提供信息。     

Demonstrating freedom from disease using multiple complex data sources 1: a new methodology based on scenario trees

Current methods to demonstrate zone or country freedom from disease are based on either quantitative analysis of the results of structured representative surveys, or qualitative assessments of multiple sources of evidence (including complex non-representative sources). This paper presents a methodology for objective quantitative analysis of multiple complex data sources to support claims of freedom from disease. Stochastic scenario tree models are used to describe each component of a surveillance system (SSC), and used to estimate the sensitivity of each SSC. The process of building and analysing the models is described, as well as techniques to take into account any lack of independence between units at different levels within a SSC. The combination of sensitivity estimates from multiple SSCs into a single estimate for the entire surveillance system is also considered, again taking into account lack of independence between components. A sensitivity ratio is used to compare different components of a surveillance system. Finally, calculation of the probability of country freedom from the estimated sensitivity of the surveillance system is illustrated, incorporating the use and valuation of historical surveillance evidence.     

Animal-based national surveillance for zoonotic disease: quality, limitations, and implications of a model system for monitoring rabies

Surveillance for zoonotic diseases among wildlife is a research and public health challenge. The inherent limitations posed by the requisite human–animal interactions are often undefined and underappreciated. The national surveillance system for animal rabies in the United States was examined as a model system; reporting of animal rabies is legally mandated, each case of rabies is laboratory confirmed, and data have been consistently collected for more than 50 years. Factors influencing the monthly counts of animal rabies tests reported during 1992–2001 were assessed by univariate and multivariable regression methods. The suitability of passively collected surveillance data for determining the presence or absence of the raccoon-associated variant of rabies within states and within individual counties was assessed by determining critical threshold values from the regression analyses. The size of the human population and total expenditures within a county accounted for 72% and 67%, respectively, of the variance in testing. The annual median number of rabies tests performed was seven for counties without rabies, 22 for counties with non-raccoon rabies, and 34 for counties with raccoon rabies. Active surveillance may be required in locales with sparse human populations when a high degree of confidence in the status of rabies is required.