Syndromic surveillance requires the acquisition and analysis of data that may be suggestive of early epidemics in a community, long before there is any categorical evidence of unusual infection. These data are often heterogenous and often quite noisey. The processs of syndromic surveillance poses problems in data integration; in selection of appropriate reusable problem-solving methods, based on task features and on the nature of the data at hand; and in mapping integrated data to appropriate problem solvers. These are all tasks that have been studied carefully in the knowledge-based systems community for many years. We demonstrate how a software architecture that suppports knoweldge-based data integrationa and problem solving facilitates many aspects of the syndromic-surveillance task. In particular, we use reference ontologies for purposes of semantic integration and a parallelizable blackboard architecture for invocation of appropriate problem solving methods and for control of reasoning. We demonstrate our results in the context of a prototype system known as the Biological Spacio-Temporal Outbreak Reasoning Module (BioSTORM), which offers an end-to-end solution to the problem of syndromic surveillance.