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.
