Improving Syndromic Surveillance by Data Integration

“症状监测”是一个通用术语，适用于各种新开发的方法， 公共卫生实践。就我们的目的而言，症状监测是指自动收集和 电子健康结果数据的近实时分析。以这种方式使用，症状监测坐在 在更广泛的“生物监测”类别中，电子数据的例行收集和分析属于 在传统的监视模式之外。 我们提出了一个研究计划，以提高性能的畸变检测方法的综合征 使用数据整合的统计方法进行监测。我们的计划侧重于三个主要领域， 潜在的改进：时态建模、时空聚类和多数据集成 溪流我们还包括一个研究翻译部分，以确保研究结果 将对卫生部门和其他症状监测从业人员有实际用途。 我们的三个具体目标是：1）开发和改善症状监测的时间模型，使用 改进的季节模型和隐马尔可夫模型（HMRM）; 2）调查和评估数据 集成方法，包括时空聚类和多数据源集成; 3）开发 符合PHIN标准的软件，供当地卫生部门和症状监测从业人员使用。

The term "syndromic surveillance" is a generic term applied to a variety of newly developed methods in public health practice. For our purposes, syndromic surveillance refers to the automated collection and analysis in near-real-time of electronic health outcome data. Used in this way, syndromic surveillance sits within a broader category of "biosurveillance", the routine collection and analysis of electronic data falling outside of the classical surveillance paradigm. We propose a research program to improve the performance of aberration detection methods for syndromic surveillance using statistical methods of data integration. Our program focuses on three main areas of potential improvement: temporal modeling, spatio-temporal clustering, and integration of multiple data streams. We also include a research translation component, in order to ensure that the results of research will be of practical use to health departments and other practitioners of syndromic surveillance. Our three specific aims are: 1) To develop and improve temporal modeling for syndromic surveillance, using improved seasonal models and Hidden Markov Models (HMMs); 2) To investigate and evaluate data integration methods, including spatio-temporal clustering and multiple data source integration; 3) To develop PHIN-compliant software for use by local health departments and syndromic surveillance practitioners.