The Centers for Disease Control defines syndromic surveillance as, “an investigational approach where health department staff, assisted by automated data acquisition and generation of statistical alerts, monitor disease indicators in real-time or near real-time to detect outbreaks of disease earlier than would otherwise be possible with traditional public health methods” (CDC, 2004). While syndromic surveillance has traditionally been used in the context of detecting natural outbreaks, it is increasingly being used to develop systems to detect bioterrorism outbreaks. Timely response to a bioterrorism event requires accurate information exchange between clinicians and public health officials. This entails building highly complex surveillance systems that provide access to heterogeneous/distributed medical data, computational resources and collaborative services, for real-time decision making in a highly reliable and secure environment. In this paper we propose enhancing syndromic surveillance through grid and autonomic computing augmentations, and present our approach to a proof of concept modeling and simulation environment.