We compared the three semantic scan methods to the standard, prodrome-based spatial scan using synthetic disease outbreaks injected into real-world emergency department data from Allegheny County, PA. We first considered 55 different outbreak types, corresponding to all distinct ICD-9 codes with at least 10 cases, which were mapped to one of the existing prodromes. For each outbreak type, we generated spatially localized injects with chief complaints sampled from the cases with that ICD-9 code (Fig. 1) . The static, dynamic and incremental methods required an average of 7.7, 7.1 and 6.9 days, respectively, to detect and were able to precisely characterize the outbreak based on the detected topic (e.g., top keywords for ICD-9 code 569.3 were 'rectal', 'bleed', and 'bleeding'). The prodrome method achieved more timely detection (5.0 days to detect) but with much less precise characterization (e.g., 'hemorrhagic' for ICD-9 code 569.3). Next, we considered both randomly selected, unmapped ICD-9 codes and synthetically generated unprecedented events, such as an outbreak that makes the patient's nose turn green. The prodrome method required 10.9 days to detect these outbreaks, while the semantic scan was able to achieve much faster detection. For example, for the green nose outbreak, the static, dynamic and incremental methods detected in 6.4, 5.3 and 5.6 days, respectively. The dynamic and incremental methods correctly identified the emerging topic (keywords 'green', 'nose', 'nasal', etc.), while the static method did not, since the outbreak did not correspond to any of the topics learned from historical data.