Syndromic surveillance systems are becoming quite common in public health departments aroundthe country. These systems allow epidemiologists and other users to monitor disease trends in theircommunities, 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 useddaily by the responsible public health personnel who rapidly become adept at identifying abnormalpatterns of disease in their communities and quickly come to recognize the patterns that describe normalseasonal variations in disease. They have little opportunity, however, to see how their normal daily viewwould change during a disease outbreak caused by a terrorist event because, luckily, few have occurredsince most systems were installed. The only way users will become familiar with how their systems reactduring a man-made outbreak is to participate in training exercises in which simulated data injected intotheir system mimics outbreak conditions. Unfortunately the creation of such exercises for these systemsis 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, theExercise/Simulation Framework (ESF), that can be used with multiple syndromic surveillance systems tocreate 'table top' exercises that mimic disease outbreaks. These exercises can be used for trainingpurposes, to help model the effect of public health response protocols such as mass prophylaxis in aspecific community, and to help develop public health response plans for use in emergency situations. Inaddition the ESF can be used to evaluate the effect of surveillance algorithms under a variety of differentdisease patterns.
