Improving Detection of Outbreaks Due to Aerosol Attacks

拟议的研究解决了早期发现生物恐怖主义袭击的问题， 将生物制剂以气溶胶的形式释放到大气中。这种袭击有可能造成数十至 成千上万的个体。早期发现风传播的攻击可以及早作出反应， 反应越早，挽救的生命就越多。 在过去的三年里，我们开发了一种算法，专门用于检测 被风吹袭。该算法的潜在优势包括： 1.由于利用了天气数据和对天气的了解， 气溶胶释放引起的暴发中病例分布的空间和时间模式。 2.在检测时将疫情部分定性为一种空气的户外气溶胶释放， 生物制剂。 3.对生物制剂释放的位置、数量和时间的最佳估计。一旦应答者 了解事件，该信息对于管理额外的监测至关重要， 反应 我们建议进一步发展的算法和衡量的程度，我们可以实现这些 效益 研究的具体目标是： 1.确定贝叶斯气溶胶释放检测器（BARD）区分 生物剂通过风传播引起的暴发和其他传播方式引起的暴发 传输 2.比较BARD检测模拟风传播疫情的性能， 没有专门设计用于检测风传播疫情的算法的性能。 3.确定BARD推理在扩展一个数后是否仍然在计算上易于处理 它的风媒疾病爆发的模型。 4.评估具体目标#3中的扩展，以改善相对于 具体目标#1和#2中使用的BARD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.