Public health surveillance systems routinely process massive volumes of data to identify health adverse events affecting the general population. Surveillance and response to foodborne disease suffers from a number of systemic and other delays that hinder early detection and confirmation of emerging contamination situations. In this paper we develop an answer set programming (ASP) application to assist public health officials in detecting an emerging foodborne disease outbreak by integrating and analyzing in near real-time temporally, spatially and symptomatically diverse data. These data can be extracted from a large number of distinct information systems such as surveillance and laboratory reporting systems from health care providers, real-time complaint hotlines from consumers, and inspection reporting systems from regulatory agencies. We encode geographic ontologies in ASP to infer spatial relationships that may not be evident using traditional statistical tools. These technologies and ontologies have been implemented in a new informatics tool, the North Carolina Foodborne Events Data Integration and Analysis Tool (NCFEDA). The application was built to demonstrate the potential of situational awareness—created through real-time data fusion, analytics, visualization, and real-time communication—to reduce latency of response to foodborne disease outbreaks by North Carolina public health personnel.