Disease Surveillance in Real Time: Geotemporal Methods

描述(由申请人提供)：实时数据收集和分析将大大加强区域和国家两级的公共卫生监测。虽然症状和诊断数据存储在行政卫生保健数据库中，但目前还没有集成这些数据的自动化系统，以便能够及时发现疾病的异常模式，这一问题在最近的生物恐怖袭击中突显出来。我们的目标是通过与现有的和快速扩展的数据获取基础设施合作来解决这一问题，以开发所需的分析工具，以便在患者开始出现在医疗保健地点时尽快识别疾病集群。 我们已经通过虚拟地实时集成多个医院急诊科(ED)数据库建立了一个监测网络。这提供了一幅区域人口疾病模式的图景。此外，在其中一家医院，我们已经收集了具有多年历史数据的回顾数据库，这些数据对于建立分析模型的正常性至关重要。 因此，该项目的目标是通过使用现成的实时信息来加强监测系统，从而提高这些系统更快发现疾病暴发的能力。由于迫切需要快速识别炭疽等生物制剂的受害者，我们将集中精力识别出现呼吸综合征的患者群。这项拟议的研究将首先建立正常的疾病模式，然后建立模型，使之能够检测出偏离这些模式的情况，并将错误警报降至最低。一旦建立了这些方法，它们就可以应用于任何其他有意义的综合征集。

DESCRIPTION (provided by applicant):Real time data collection and analysis would substantially strengthen public health surveillance at both regional and national levels. Although symptom and diagnostic data are stored in administrative health care databases, there currently are no automated systems for integrating this data so that abnormal patterns of disease can be detected in a timely manner, a problem highlighted by recent bioterrorist attacks. We aim to address this by working with an existing and rapidly expanding data acquisition infrastructure to develop the analytic tools needed to recognize disease clusters as soon as possible once patients begin appearing at health care sites. We have already established a surveillance network by virtually integrating multiple hospital emergency department (ED) databases in real time. This provides a picture of regional population patterns of disease. Furthermore, at one of these hospitals, we have assembled retrospective databases with many years of historical data critical for establishing normality for the analytic models. Thus, the goal of this project is to strengthen surveillance systems by using readily available information in realtime, thereby increasing the power of these systems to detect disease outbreaks sooner. Because of the urgent need to quickly identify victims of biowarfare agents such as anthrax, we will concentrate on identifying clusters of patients presenting with respiratory syndromes. The proposed study will first establish normal patterns of disease and then build models that enable the detection of deviations from these patterns with a minimum of false alarms. Once these methods are established, they can be applied to any other meaningful set of syndromes.