Collaborative Research for Developing ATD: Bayesian Methods in Syndromic Surveillance: CAR Models and Computational Implementation
开发 ATD 的协作研究:症状监测中的贝叶斯方法:CAR 模型和计算实现
基本信息
- 批准号:0914603
- 负责人:
- 金额:$ 4.34万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-08-15 至 2012-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The collaborative team develops and implements computationally of a Bayesianformulation of syndromic surveillance targeted at early detection of diseaseoutbreaks. The research spans statistical theory and methodology, as well asissues of algorithms and computational science. Conditional autoregressivemodels, or CAR models, form the basis of the formulation, in order toaccommodate spatial as well as temporal effects. The principal challenges arescalability (7,500 major U.S. hospitals are linked into the reportingnetwork), multivariate reports, complex dependence structure in the data, lowdata quality, and incorporation of regional and other covariates. The unifyingtheme is principled calculation of uncertainties.The research is framed by an application of national significance. There isenormous social and economic benefit from early discovery of disease. Thestakes are immense: the US Office of Technology Assessment estimates that arelease of 100 KG of anthrax spores upwind of Washington, DC would, if notdetected rapidly, lead to as many as three million deaths and a trilliondollars of economic losses. The research produces actionable, easilyinterpretable information that supports decision makers: a clear statement ofthe probability that a particular disease is present somewhere in the US, orpresent in a particular city, together with the associated uncertainty.False alarm rates are controlled, making them commensurate with resourcesavailable for post-signal investigation and taking into accountconsequences such as the psychological and financial impacts of a false alarm.
合作小组开发和实施了以疾病暴发早期检测为目标的贝叶斯症状监测的贝叶斯公式。这项研究涵盖了统计理论和方法论,以及算法和计算科学。条件自回归模型或CAR模型构成了公式的基础,以适应空间和时间效应。主要的挑战是可扩展性(7,500家美国主要医院连接到报告网络中)、多变量报告、数据中复杂的依赖结构、低数据质量以及纳入区域和其他协变量。统一的主题是不确定度的原则性计算。这项研究的框架是一个具有国家意义的应用程序。及早发现疾病有巨大的社会和经济效益。风险是巨大的:美国技术评估办公室估计,如果不能迅速发现,华盛顿特区上风方向至少100公斤的炭疽孢子将导致多达300万人死亡和1万亿美元的经济损失。这项研究产生了可操作的、易于解释的支持决策者的信息:一种特定疾病在美国某地或特定城市存在的可能性以及相关的不确定性的清晰陈述。错误报警率受到控制,使其与可用于信号后调查的资源相称,并考虑到虚假警报的心理和经济影响等后果。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Gauri Datta其他文献
Gauri Datta的其他文献
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{{ truncateString('Gauri Datta', 18)}}的其他基金
Cross-Sectional and Time Series Approaches to Small Area Estimation: Methods and Applications
小区域估计的横截面和时间序列方法:方法和应用
- 批准号:
0241651 - 财政年份:2003
- 资助金额:
$ 4.34万 - 项目类别:
Continuing Grant
Asymptotic Approaches to Bayesian and Likelihood Inference
贝叶斯和似然推理的渐近方法
- 批准号:
0071642 - 财政年份:2000
- 资助金额:
$ 4.34万 - 项目类别:
Standard Grant
Parametric Empirical Bayes Point and Interval Estimation in Small Area Estimation from Complex Surveys
复杂调查小区域估计中的参数经验贝叶斯点和区间估计
- 批准号:
9705145 - 财政年份:1997
- 资助金额:
$ 4.34万 - 项目类别:
Standard Grant
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