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The threat of infectious disease outbreaks and bioterrorism attacks has stimulated the development of syndromic surveillance systems, which focus on using pre-diagnostic data such as emergency department chief complaints and over-the-counter (OTC) drug sales to detect bioterrorism events in a timely manner. A key function of syndromic surveillance systems is detecting possible bioterrorism events from time series data. In this paper, we propose a novel temporal outbreak detection method based on the Markov switching model, a special case of hidden Markov models. The model is motivated to address several computational problems with existing detection schemes concerning the inconsistency in parameter estimation and the resulting undesired detection performance. Preliminary evaluation using simulated outbreaks injected on authentic time series shows that our method outperforms benchmark methods in terms of outbreak detection speed and detection sensitivity at given levels of false alarm rates.