Collaborative Research: ATD: Sequential Quickest Detection and Identification of Multiple Co-dependent Epidemic Outbreaks

合作研究：ATD：多种相互依赖的流行病爆发的顺序最快检测和识别

• 批准号: 1222262
• 负责人: Michael Ludkovski
• 金额: $ 21.26万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1222262&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Sequential; Quickest

This project is key to the development of next generation quantitative algorithms for detection of epidemic outbreaks. The investigators address two focus problems that arise in epidemic surveillance, namely that of quickest detection of (a) spatially and (b) pathogen heterogeneous outbreaks. An early and accurate response is achieved by taking advantage of the co-dependent nature of the corresponding syndromic observations and by appropriate modeling of this dependency. To this end, the investigators develop innovative online quickest detection and sequential classification techniques to analyze multiple correlated data streams undergoing distinct changes. These techniques are assessed through their ability to optimally issue timely outbreak alerts with minimal false alarm rates. Moreover, the investigators address the problem of early detection and identification of an epidemic outbreak by designing a simultaneous min-max change-point detection and classification algorithm of a single data stream with unknown post-disorder characteristics. In this way, the investigators are able to also address the problem of model uncertainty and build robust algorithms. Finally, the investigators combine their expertise by carrying out a multi-faceted comparison of alternative formulations (especially Bayesian versus min-max) for the focus problems, thus creating a model-free state-of-the-art toolkit targeting highly complex bio-surveillance data.Statistical and mathematical methods are essential to address some of the manifold challenges presented by the threat of infectious epidemics. This project is vital to the improvement of public health infrastructure for effective epidemic countermeasures. The investigators build innovative techniques for the early detection and pathogen-type classification of epidemic outbreaks spanning multiple geographic sites by taking advantage of the co-dependent nature of such outbreaks. The developed methods will be directly communicated to public health epidemiologists through outreach activities. Thus, this project is expected to improve the effectiveness of bio-surveillance and contribute to the health and well-being of our communities at large. The interdisciplinary nature of the research activities assists in the training of graduate and undergraduate students and expands the exchange of ideas between Brooklyn College, the Graduate Center of CUNY and UC Santa Barbara. The PIs? techniques constitute an innovative breakthrough in the general methodology of detection and identification of threats in related but distinct streams of observations. Thus, they provide a state-of-the-art platform for threat detection and classification in other areas of engineering such as communications, network intrusion and others.

该项目是开发用于检测流行病爆发的下一代定量算法的关键。 研究人员解决了流行病监测中出现的两个焦点问题，即最快检测（a）空间和（b）病原体异质性爆发的问题。通过利用相应症状观察的相互依赖性质并通过对这种依赖关系进行适当的建模，可以实现早期且准确的响应。 为此，研究人员开发了创新的在线最快检测和顺序分类技术来分析经历不同变化的多个相关数据流。 这些技术通过其以最小的误报率最佳地及时发出疫情警报的能力进行评估。此外，研究人员通过设计具有未知病后特征的单个数据流的同时最小-最大变化点检测和分类算法，解决了流行病爆发的早期检测和识别问题。 通过这种方式，研究人员还能够解决模型不确定性的问题并构建稳健的算法。最后，研究人员结合他们的专业知识，对焦点问题的替代方案（尤其是贝叶斯与最小-最大）进行多方面比较，从而创建了一个针对高度复杂的生物的无模型的最先进工具包。监测数据。统计和数学方法对于解决传染病流行威胁带来的一些多重挑战至关重要。该项目对于改善公共卫生基础设施、有效应对疫情至关重要。研究人员利用跨多个地理位置的流行病爆发的相互依赖性质，建立了创新技术，用于早期检测和病原体类型分类。开发的方法将通过外展活动直接传达给公共卫生流行病学家。因此，该项目预计将提高生物监测的有效性，并为我们整个社区的健康和福祉做出贡献。研究活动的跨学科性质有助于培养研究生和本科生，并扩大布鲁克林学院、纽约市立大学研究生中心和加州大学圣巴巴拉分校之间的思想交流。 PI？技术构成了在相关但不同的观察流中检测和识别威胁的一般方法的创新突破。因此，它们为通信、网络入侵等其他工程领域的威胁检测和分类提供了最先进的平台。