Automated biosurveillance data from England and Wales, 1991-2011.
Automated biosurveillance data from England and Wales, 1991-2011.
复制标题
DOI：
10.3201/eid1901.120493
复制
发表时间：
2013-01
影响因子：
11.8
通讯作者：
Farrington CP
中科院分区：
文献类型：
--
作者：
Enki DG;Noufaily A;Garthwaite PH;Andrews NJ;Charlett A;Lane C;Farrington CP
二十年的数据为大型自动化疫情检测系统的设计提供了宝贵的见解。用于非常大的多个监测数据库的爆发检测系统必须既适合于可用的数据，又适合于完全自动化的要求。为了开发更有效的疫情检测算法，我们分析了英格兰和威尔士用于疫情检测的大型实验室监测数据库20年（1991-2011年）的数据。这些数据涉及3，303种不同类型的传染性病原体，频率范围跨越6个数量级。每周都有数百种微生物报告。我们描述了多样性的季节性模式，趋势，文物，和额外的泊松变化，一个有效的多个实验室为基础的爆发检测系统必须调整。我们提供的经验信息，以指导选择简单的统计模型自动监测多种生物体，在这样的爆发检测系统的关键要求，即，鲁棒性，灵活性和灵敏度。
Twenty years of data provide valuable insights for the design of large automated outbreak detection systems. Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991–2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.