Automated Surveillance of Overlapping Outbreaks and New Outbreak Diseases

重叠暴发和新暴发疾病的自动监测

• 批准号: 10460909
• 负责人: GREGORY F. COOPER
• 金额: $ 33.79万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-03 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10460909
• 关键词: Accident and Emergency department; Adenoviruses; Algorithms; Caring; Characteristics; Clinical; Communicable Diseases; County; Couples; Dangerousness; Data; Data Set; Decision Making; Detection; Diagnosis; Disease; Disease Outbreaks; Disease Surveillance; Disease model; Epidemiology; Evaluation; Health; Healthcare Systems; Human; Humidity; Individual; Influenza; Influenza A virus; Influenza B Virus; Laboratories; Logistics; Masks; Methods; Modeling; Morbidity - disease rate; Natural Language Processing; Output; Patients; Pennsylvania; Performance; Population; Probability; Public Health; Reporting; Research; Research Personnel; Research Support; Respiratory Disease; Respiratory syncytial virus; Sales; Stress; Symptoms; System; Testing; Thermometers; Time; Validation; emerging pathogen; improved; individual patient; influenza outbreak; innovation; mortality; novel; pathogenic virus; population health; respiratory; respiratory pathogen

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.

项目摘要/摘要 该项目将开发和评估自动检测和表征传染性疾病的新方法。 呼吸道疾病。这些方法将在它们检测和表征(1)多个， 已知疾病的重叠暴发，这是一种常见的情况，(2)新的、 新出现的疾病，这可能是危险的，以及(3)1和2的组合同时发生。这个 在发生其他常见疫情的情况下，及早发现新疾病的能力可能尤其重要 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疫情检测和定性，包括紧急情况 卫生部的报告，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，该系统 调查人员已经开发出。该系统将用于执行疫情检测和 特征，并输出可能正在发生的不同可能的疾病爆发的概率， 以及它们的特征，如它们的可能开始时间和流行病学曲线。独一无二的一面 该系统的特点是能够使用来自个别患者临床报告的数据，例如急诊科 报告。系统将自然语言处理应用于报告，以得出一组症状、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 关于每个病人的疾病。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暴发的证据。 该项目将使用宾夕法尼亚州阿勒格尼县的模拟数据和真实数据进行评估。会的 重点关注四种常见的暴发疾病，即甲型流感、乙型流感、呼吸道合胞病毒、 和腺病毒。评估将检查该系统能够(1)检测和表征多个 重叠的疾病暴发，(2)检测新的暴发疾病并创建准确的临床 描述它(使用留一交叉验证方法)，以及(3)使用各种数据类型来 改进疫情检测和定性。 本研究提出的创新是一种新颖的、集成的、概率的方法，用于早期和 准确检测威胁公共健康的疾病暴发。提出的方法具有重要的意义 有可能改善临床医生和公共卫生官员可获得的信息，这可以预期 改善临床和公共卫生决策，最终改善人口健康。