Improving Detection of Outbreaks Due to Aerosol Attacks

拟议的研究解决了及早发现生物恐怖分子袭击的问题 将生物制剂以气雾剂的形式释放到大气中。这类袭击有可能导致数十至 成千上万的个体。及早检测到风载攻击能够及早做出响应并 反应越早，拯救的生命就越多。 在过去的三年里，我们开发了一种专门设计的算法来检测由 被风吹来的攻击。该算法的潜在优势包括： 1.由于利用了天气数据和对 气雾剂释放引起的暴发病例分布的空间和时间模式。 2.在检测到暴发时，将其部分定性为室外气溶胶释放的一种 生物制剂。 3.对生物制剂释放的地点、数量和时间的最佳估计。曾经的应答者 都知道事件，此信息对于管理额外的监视和 回应。 我们建议进一步开发算法，并衡量我们可以实现这些目标的程度 福利。 研究的具体目的是： 1.确定贝叶斯气溶胶释放探测器(BARD)区分 由风媒传播生物制剂引起的疫情和由其他方式引起的疫情 变速箱。 2.将BARD在检测模拟风媒疫情方面的性能与 不是专门为检测风媒疫情而设计的算法的性能。 3.确定与BARD的推论在扩展一个数字后是否在计算上仍然容易处理 它的风媒疫情模型。 4.评估在特定目标#3中进行的扩展，以提高相对于以下各项的检测性能 在具体目标#1和#2中使用的巴德的基线版本。

The proposed research addresses the problem of early detection of bioterrorism attacks where bioterrorists ¿elease a biological agent into the atmosphere as an aerosol. Such attacks have the potential to kill tens to hundreds of thousands of individuals. Early detection of a windborne attack enables an early response and the earlier the response, the greater the number of lives saved. Over the past three years, we have developed an algorithm designed specifically to detect outbreaks caused by windborne attacks. The potential advantages of this algorithm include: 1. Earlier detection at a lower false-alarm rate as a result of utilizing weather data and knowledge of the spatial and temporal patterns in case distributions in an outbreak resulting from an aerosol release. 2. Partial characterization of the outbreak at the time of detection as an outdoor, aerosol release of a biological agent. 3. A best estimate of location, quantity, and timing of the release of the biological agent. Once responders are aware of an event, this information is essential for the management of additional surveillance and response. We propose to further develop the algorithm and measure the degree to which we can achieve these benefits. The specific aims of the research are to: 1. Determine the ability of the Bayesian Aerosol Release Detector (BARD) to discriminate between outbreaks caused by windborne transmission of biological agents and outbreaks due to other modes of transmission. 2. Compare the performance of BARD for the detection of simulated windborne outbreaks with the performance of algorithms not specifically designed to detect windborne outbreaks. 3. Determine whether inference with BARD remains computationally tractable after extending in a number of ways its models of windborne outbreaks. 4. Evaluate the extensions made in Specific Aim #3 for improvements in detection performance relative to the baseline versions of BARD used in Specific Aims #1 and #2.