Automated Surveillance of Overlapping Outbreaks and New Outbreak Diseases

项目总结/摘要 该项目将开发和评估用于自动检测和表征传染性疾病的新方法。 呼吸道疾病。该方法在检测和表征（1）多种， 已知疾病的重叠爆发，这是一种常见的情况，（2）一种新的， 新出现的疾病，这可能是危险的，和（3）1和2的组合同时发生。的 在其他常见疾病爆发的背景下，早期发现新疾病的能力可能特别重要， 如果这种疾病导致严重疾病并在人群中迅速传播，这一点很重要。新方法可以 还使用各种数据来执行爆发检测和表征，包括紧急 部门报告、实验室结果、该地区的零售温度计销售以及当地与健康相关的推文。 这些新方法将建立在现有贝叶斯概率系统的框架上， 研究人员开发了。该系统将用于执行爆发检测的数据作为输入， 表征，并输出可能发生的不同可能疾病爆发的概率， 以及它们的特征，例如它们可能的开始时间和流行病学曲线。一个独特方面 该系统的一个优点是它能够使用来自单个患者临床报告的数据，例如急诊科 报道该系统将自然语言处理应用于报告以导出一组症状、体征和 其他发现。然后，它使用这些发现和概率疾病模型来推导概率分布 对每个病人的疾病进行评估。对于最近看到的许多患者，该系统使用他们的 概率分布作为检测和表征疾病爆发的证据。 该项目将使用模拟数据和来自宾夕法尼亚州阿勒格尼县的真实的数据进行评估。它将 重点关注四种常见的爆发性疾病，即甲型流感、乙型流感B、呼吸道合胞病毒、 和腺病毒。该评估将检查系统在多大程度上能够（1）检测和表征多个 （2）发现新的暴发疾病，并建立准确的临床诊断 它的描述（使用留一交叉验证方法），以及（3）使用各种数据类型来 改进爆发检测和表征。 这项研究所提出的创新是一种新颖的，综合的，概率性的方法，用于早期和 准确检测威胁公共卫生的疾病爆发。所提出的方法具有重要意义 有可能改善临床医生和公共卫生官员可获得的信息， 改善临床和公共卫生决策，并最终改善人口健康。

Project Summary / Abstract This project will develop and evaluate new methods for automated detection and characterization of infectious respiratory diseases. The methods will be novel in their ability to detect and characterize (1) multiple, overlapping outbreaks of known diseases, which is a situation that occurs commonly, (2) an outbreak of a new, emerging disease, which can be dangerous, and (3) a combination of 1 and 2 occurring at the same time. The ability to detect a new disease early, in the context of other common outbreaks occurring, may be particularly important if the disease causes serious illness and spreads rapidly in the population. The new methods can also use a wide variety of data to perform outbreak detection and characterization, including emergency department reports, laboratory results, retail thermometer sales in the region, and local health-related tweets. These new methods will be built upon the framework of an existing Bayesian, probabilistic system, which the investigators have developed. This system takes as input data used to perform outbreak detection and characterization, and it outputs the probabilities of different possible disease outbreaks that may be occurring, as well as their characteristics, such as their probable start times and epidemiological curves. A unique aspect of the system is its ability to use data from individual patient clinical reports, such as emergency department reports. The system applies natural language processing to the reports to derive a set of symptoms, signs, and other findings. It then uses these findings and probabilistic disease models to derive a probability distribution over the diseases for each patient. For the many patients seen in the recent past, the system uses their probability distributions as evidence in detecting and characterizing disease outbreaks. The project will be evaluated using simulated data and real data from Allegheny County, Pennsylvania. It will focus on four common outbreak diseases, namely, influenza A, influenza B, respiratory syncytial virus (RSV), and adenovirus. The evaluation will examine how well the system can (1) detect and characterize multiple overlapping outbreaks of disease, (2) detect a new outbreak disease and create an accurate clinical description of it (using a leave-one-out cross validation approach), and (3) use a variety of data types to improve outbreak detection and characterization. The innovation being advanced by this research is a novel, integrated, probabilistic approach for the early and accurate detection of disease outbreaks that threaten public health. The proposed approach has significant potential to improve the information available to clinicians and public health officials, which can be expected to improve clinical and public health decision making, and ultimately to improve population health.