ITR: Bayesian Modeling for Biosurveillance

ITR：生物监测贝叶斯建模

• 批准号: 0325581
• 负责人: Gregory Cooper
• 金额: $ 351.88万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325581&HistoricalAwards=false
• 关键词: ITR; Bayesian; Modeling; Biosurveillance

Early, reliable detection of outbreaks of disease, whether natural (e.g., West Nile virus) or bioterrorist-induced (e.g., anthrax and smallpox), is a critical problem today. It is important to detect outbreaks early in order to provide the best possible medical response and treatment, as well as to improve the chances of identifying the source. A primary goal of this project is to develop basic new Bayesian probabilistic inference algorithms that monitor electronically available healthcare data to achieve early, reliable detection of outbreaks. In particular, inference will take place on Bayesian networks that model the links between available data and possible causes of outbreaks. The scientific challenge of monitoring for outbreaks within an entire population create major computational challenges in building and applying Bayesian models that are orders of magnitude larger than those developed previously. The project will apply and extend state-of-the-art probabilistic inference methods to achieve efficient inference. If inference indicates that an outbreak is likely, an alert will be raised automatically. Appropriately, however, public health officials are unlikely to blindly trust an outbreak alert, unless there is an explanatory justification. Automated explanation of Bayesian inference is therefore another key project goal. The scientific contributions of this project will follow from developing, investigating, and evaluating new modeling and algorithmic techniques that make Bayesian biosurveillance practical for monitoring and diagnosing (in real time) the disease-outbreak status of an entire population. In investigating these issues, this project is anticipated to make both specific scientific contributions to computer science, statistics, and public health, as well as broader contributions to public safety.

早期可靠地检测疾病的爆发，无论是自然的（例如，西尼罗河病毒）或生物恐怖分子诱导的（例如，炭疽和天花）是当今的一个严重问题。必须及早发现疫情，以便提供尽可能好的医疗应对和治疗，并提高查明源头的机会。该项目的主要目标是开发基本的新贝叶斯概率推理算法，用于监测电子医疗数据，以实现早期，可靠的疫情检测。特别是，推理将在贝叶斯网络上进行，该网络对现有数据与爆发可能原因之间的联系进行建模。 在整个人群中监测疫情的科学挑战在构建和应用比以前开发的模型大几个数量级的贝叶斯模型方面带来了重大的计算挑战。该项目将应用和扩展最先进的概率推理方法，以实现有效的推理。如果推断表明有可能爆发，将自动发出警报。然而，公共卫生官员不太可能盲目地相信疫情警报，除非有解释性的理由。因此，贝叶斯推理的自动解释是另一个关键项目目标。该项目的科学贡献将来自开发，调查和评估新的建模和算法技术，使贝叶斯生物监测实用于监测和诊断（真实的时间）整个人群的疾病爆发状态。在调查这些问题时，预计该项目将对计算机科学、统计学和公共卫生做出具体的科学贡献，并对公共安全做出更广泛的贡献。