Increased concern about bioterrorism and emerging diseases is driving the development of systems for early epidemic detection. These systems have different requirements than traditional public health monitoring systems. They typically deal with larger and more diverse data sets and must use a large variety of analysis techniques to perform real-time tracking of multiple syndromes. Operationally, these systems must be able to perform rapid analysis on large data sets. A highly configurable system is also required to enable dynamic adaptation of outbreak detection algorithms to accommodate changing data streams and disease models. To meet the needs of these systems, we have developed a knowledge-based framework for deploying surveillance problem solvers. We show how we are using this architecture in a surveillance system that uses a variety of problem solvers to perform outbreak detection.
