Detection of spatial and spatio-temporal clusters
Detection of spatial and spatio-temporal clusters
复制标题
DOI：
--
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
A. Moore;Daniel B. Neill
中科院分区：
文献类型：
--
作者：
A. Moore;Daniel B. Neill
本文为空间和时空簇的自动检测开发了一个通用的、功能强大的统计框架。我们的“广义空间扫描”框架是一个灵活的、基于模型的框架，用于在不同的应用领域中进行准确和计算高效的集群检测。通过快速空间扫描算法和新的贝叶斯簇检测方法的发展，我们现在可以比以前的方法快数百或数千倍地检测簇。更及时地检测新出现的集群(具有高检测能力和低假阳性率)是通过开发基于预期的扫描统计实现的，该统计从过去的数据中学习基线模型，然后根据这些期望检测出异常区域。这些集群检测方法被应用于两个真实世界的问题领域：对新出现的疾病流行的早期检测，以及对fMRI脑成像数据中的活动集群的检测。这项工作的一个主要贡献是开发了用于全国疾病监测的SSS系统，目前几个州和地方卫生部门在日常实践中使用该系统。该系统从全国20,000多家商店和医院接收数据(包括急诊科记录和药品销售)，自动检测新出现的疾病集群，并将结果报告给公共卫生官员。通过回溯性案例研究和半合成测试，我们已经表明，我们的系统可以显著快于以前的疾病监测方法来检测疫情。
This thesis develops a general and powerful statistical framework for the automatic detection of spatial and space-time clusters. Our "generalized spatial scan" framework is a flexible, model-based framework for accurate and computationally efficient cluster detection in diverse application domains. Through the development of the "fast spatial scan" algorithm and new Bayesian cluster detection methods, we can now detect clusters hundreds or thousands of times faster than previous approaches. More timely detection of emerging clusters (with high detection power and low false positive rates) was made possible by development of "expectation-based" scan statistics, which learn baseline models from past data then detect regions that are anomalous given these expectations. These cluster detection methods were applied to two real-world problem domains: the early detection of emerging disease epidemics, and the detection of clusters of activity in fMRI brain imaging data. One major contribution of this work is the development of the SSS system for nationwide disease surveillance, currently used in daily practice by several state and local health departments. This system receives data (including emergency department records and medication sales) from over 20,000 stores and hospitals nationwide, automatically detects emerging clusters of disease, and reports these results to public health officials. Through retrospective case studies and semi-synthetic testing, we have shown that our system can detect outbreaks significantly faster than previous disease surveillance methods.