Methods for evaluating bioterrorism surveillance tools

产品说明： 我们怎么知道生物恐怖分子会袭击我们？ 自2001年10月恐怖分子传播炭疽病以来，人们一直担心该国易受生物恐怖主义的影响，即生物制剂的传播易受恐怖主义的影响。因此，越来越多的资源被用于旨在发现这种攻击的监测系统。一种有希望的检测方法是监测初级保健医生的就诊情况。这种方法依赖于这样一个事实，即许多潜在的生物恐怖主义制剂引起非特异性的早期症状。如果可以通过这些早期症状检测到攻击，那么治疗，预防和遏制可以比需要明确诊断更早开始。然而，通过监测早期症状进行检测是困难的，因为生物恐怖主义的“信号”必须根据自然发生的疾病的“噪音”进行检测。 已经提出了几种统计算法，这种信号检测，但是，相对较少的是已知的方法的相对性能。这是一个重要的问题，因为在给定相同数据的情况下，某些统计技术相对于其他统计技术可以实现显著改进的检测。 有两个主要障碍阻碍技术的比较：第一，生物恐怖袭击必须模拟，因为它们具有排除简单化和概括化的特点。我们通过创建一个复杂的微观模拟来描述炭疽攻击的影响以及它如何出现在我们运行的监视系统中来解决这个问题。其次，必须建立可行的比较指标。在这种情况下，相对简单的方法，如ARL或ROC曲线，是不够的，因为它们忽略了关键的功能，如检测的及时性，不同的假阳性率的方法的可变排名，和/或受影响的人数。 在这个应用程序中，我们建议开发工具来比较检测生物恐怖袭击的统计方法。我们将探索：1）加权ROC曲线; 2）广义多维ROC曲面; 3）基于成本的评估，包括调查和假阳性成本以及每种方法所产生和避免的死亡率和发病率的值。

DESCRIPTION: How will we know when a bioterrorist attacks us? Since the terrorist dissemination of anthrax in October of 2001, there has been concern about the nation's vulnerability to bioterrorism, that is, to terrorism by the spread of biological agents. Increasing resources have thus been devoted to surveillance systems intended to detect such attacks. One promising detection method is to monitor visits to primary care physicians. This approach relies on the fact that many potential bioterrorism agents cause early symptoms that are non-specific. If an attack can be detected through these early symptoms, then treatment, prophylaxis, and containment can be started earlier than if definitive diagnoses are required. However, detection through surveillance of early symptoms is difficult since the 'signal' of the bioterrorism must be detected against the 'noise' of naturally occurring disease. Several statistical algorithms have been proposed for this signal detection; however, relatively little is known about the relative performance of the methods. This is an important question, as substantially improved detection may be achieved by some statistical techniques relative to others, given the same data. There are two major obstacles impeding the comparison of techniques: First, bioterrorist attacks must be simulated, since they have traits that rule out simplification and generalization. We have addressed this problem by creating a complex microsimulation to describe the effects of an anthrax attack and how it would appear in a surveillance system we operate. Second, viable metrics for comparison must be created. In this case, relatively simple methods such as ARLs or ROC curves, are insufficient, since they ignore crucial features, such as timeliness of detection, variable rankings of methods for different false positive rates, and/or the number of people affected. In this application, we propose developing tools to compare statistical methods for detection of bioterrorist attack. We will explore: 1) weighted ROC curves; 2) generalized multidimensional ROC surfaces; and 3) cost-based evaluation incorporating investigation and false positive costs as well as the value of mortality and morbidity incurred and averted by each method.