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.

如今，早期，可靠的疾病暴发（例如，西尼罗河病毒）还是生物疗法诱导的（例如炭疽和天花）是一个关键问题。重要的是要尽早检测疫情，以提供最佳的医疗反应和治疗，并提高识别来源的机会。该项目的主要目标是开发基本的新贝叶斯概率推理算法，以监控电子可用的医疗保健数据，以实现对暴发的早期可靠检测。特别是，推理将在贝叶斯网络上进行，该网络对可用数据和可能的爆发原因之间的链接进行建模。 监测整个人群中暴发的科学挑战在建立和应用比以前开发的模型大的贝叶斯模型方面引起了重大的计算挑战。该项目将应用并扩展最新的概率推理方法，以实现有效的推理。如果推论表明可能发生爆发，则将自动提高警报。但是，适当地，除非有解释性理由，否则公共卫生官员不太可能盲目信任爆发警报。因此，贝叶斯推论的自动解释是另一个关键项目目标。该项目的科学贡献将遵循开发，研究和评估新的建模和算法技术，这些技术使贝叶斯生物监视剂可实用，可（实时监测和诊断）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，统计和公共卫生做出具体的科学贡献，以及对公共安全的更广泛贡献。