Improving Detection of Outbreaks Due to Aerosol Attacks

改进对气溶胶攻击造成的疫情爆发的检测

• 批准号: 7098625
• 负责人: WILLIAM R HOGAN
• 金额: $ 43.99万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2008-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7098625
• 关键词: air pollution; air sampling /monitoring; biohazard detection; bioterrorism /chemical warfare; communicable disease diagnosis; computer assisted diagnosis; computer data analysis; computer program /software; computer simulation; diagnosis design /evaluation; diagnosis quality /standard; disease outbreaks; early diagnosis; environmental health; human data; public health; rapid diagnosis

The proposed research addresses the problem of early detection of bioterrorism attacks where bioterrorist release 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.

拟议的研究解决了生物恐怖袭击的早期检测问题，生物恐怖分子将生物制剂作为气溶胶释放到大气中。这种攻击有可能杀死数万至数十万人。及早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，我们开发了一种专门用于检测由风传播攻击引起的疫情的算法。该算法的潜在优点包括：1.由于利用气象数据和对气溶胶释放引起的疫情分布的空间和时间模式的了解，以较低的误报率及早发现。2.在检测时将疫情部分定性为一种生物剂的户外气溶胶释放。3.对生物制剂释放的位置、数量和时间的最佳估计。一旦响应者意识到事件，该信息对于管理额外的监测和响应至关重要。我们建议进一步发展的算法，并衡量在何种程度上，我们可以实现这些好处。确定贝叶斯气溶胶释放探测器（BARD）区分由生物剂的风媒传播引起的暴发和由其他传播方式引起的暴发的能力。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.确定在以多种方式扩展其风传疫情模型后，BARD的推断是否仍然在计算上易于处理。4.评价特定目标#3中的扩展，相对于特定目标#1和#2中使用的BARD基线版本的检测性能改进。