Syndromic surveillance systems are becoming quite common in public health departments around the country. These systems allow epidemiologists and other users to monitor disease trends in their communities, generally with the primary objective of identifying and acting on unusual disease patterns, whether natural or man-made, as quickly as possible. Most of the active surveillance systems are used daily by the responsible public health personnel who rapidly become adept at identifying abnormal patterns of disease in their communities and quickly come to recognize the patterns that describe normal seasonal variations in disease. They have little opportunity, however, to see how their normal daily view would change during a disease outbreak caused by a terrorist event because, luckily, few have occurred since most systems were installed. The only way users will become familiar with how their systems react during a man-made outbreak is to participate in training exercises in which simulated data injected into their system mimics outbreak conditions. Unfortunately the creation of such exercises for these systems is a complex task which is beyond the scope of most health departments. The purpose of this project is to produce aframework of standards and software tools, the Exercise/Simulation Framework (ESF), that can be used with multiple syndromic surveillance systems to create 'table top' exercises that mimic disease outbreaks. These exercises can be used for training purposes, to help model the effect of public health response protocols such as mass prophylaxis in a specific community, and to help develop public health response plans for use in emergency situations. In addition the ESF can be used to evaluate the effect of surveillance algorithms under a variety of different disease patterns.