US/China Digital Government Collaboration: US-China Infectious Disease Informatics and BioSurveillance Workshop

该奖项将支持一个美中合作研讨会，在传染病信息学的大背景下交流信息并制定综合征监测（定义见下文）的研究议程。该讲习班将于1997年年底在中国北京举行，与会者包括美国和中国的研究人员和卫生从业人员，包括政府和学术专家。由于人们日益关注自然灾害或生物恐怖主义袭击造成的致命和代价高昂的传染病威胁，因此举办这一主题的讲习班是适当的。需要新的方法来查明和跟踪新出现的传染病和流行病的爆发。虽然传统的疾病监测往往依赖耗时的实验室诊断，而且报告应通报疾病的速度往往缓慢和不完整，但一种新的公共卫生监测系统有可能大大加快对疾病暴发的发现。这些新的基于计算机的监测系统为医院以及州、地方和联邦卫生官员提供了有价值和及时的信息。它们能够实时或接近实时地检测严重疾病和潜在的生物恐怖主义剂暴露，从而能够迅速作出公共卫生反应。这种公共卫生监测方法通常被称为综合征监测，其定义为“持续、系统地收集、分析和解释特定于‘综合征’的数据，以早期发现公共卫生异常”。综合征监测背后的基本原理在于，可以通过及时显示的综合征表现来监测感兴趣的特定疾病，例如护士呼叫、药物购买和学校或工作缺勤。除了监测疾病的早期发现和报告外，综合征监测还提供了丰富的数据存储库和高度活跃的通信系统，用于情况认识和事件表征。多个参与者在不同的和地理上分离的信息来源之间提供相互联系，以促进对不断变化的形势的清晰理解。来自广泛背景的研究人员将参与其中，包括但不限于流行病学、统计学、应用数学、信息系统、计算机科学和机器学习/数据挖掘。大约25个人将参加。

This award will support a US-China collaborative workshop to exchange information and develop a research agenda for syndromic surveillance (defined below) within the larger context of infectious disease informatics. The workshop will take place in late 1997 in Beijing China, with attendees consisting equally of US and Chinese researchers and health practitioners, including government and academic experts. A workshop in this topic is appropriate because of increasing concern over the deadly and costly threats of infectious diseases caused by natural disasters or bioterrorism attacks. New methodologies are needed for identifying and tracking emerging infectious diseases and epidemic outbreaks. While traditional disease surveillance often relies on time-consuming laboratory diagnosis and the reporting of notifiable diseases is often slow and incomplete, a new breed of public health surveillance systems has the potential to significantly speed up detection of disease outbreaks. These new, computer-based surveillance systems offer valuable and timely information to hospitals as well as to state, local, and federal health officials. They are capable of real-time or near real-time detection of serious illnesses and potential bioterrorism agent exposures, allowing for a rapid public health response. This public health surveillance approach is generally called syndromic surveillance, which is defined as "an ongoing, systematic collection, analysis, and interpretation of 'syndrome'-specific data for early detection of public health aberrations." The rationale behind syndromic surveillance lies in the fact that specific diseases of interest can be monitored by syndromic presentations that can be shown in a timely manner such as nurse calls, medication purchases, and school or work absenteeism. In addition to early detection and reporting of monitored diseases, syndromic surveillance also provides a rich data repository and highly active communication system for situation awareness and event characterization. Multiple participants provide interconnectivity among disparate and geographically separated sources of information to facilitate a clear understanding of the evolving situation. Researchers from a wide range of backgrounds will participate, including but not limited to epidemiology, statistics, applied mathematics, information systems, computer science and machine learning/data mining. Approximately 25 individuals will participate.