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Accurate and timely detection of infectious disease outbreaks provides valuable information which can enable public health officials to respond to major public health threats in a timely fashion. However, disease outbreaks are often not directly observable. For surveillance systems used to detect outbreaks, noises caused by routine behavioral patterns and by special events can further complicate the detection task. Most existing detection methods combine a time series filtering procedure followed by a statistical surveillance method. The performance of this "two-step?? detection method is hampered by the unrealistic assumption that the training data are outbreak-free. Moreover, existing approaches are sensitive to extreme values, which are common in real-world data sets. We considered the problem of identifying outbreak patterns in a syndrome count time series using Markov switching models. The disease outbreak states are modeled as hidden state variables which control the observed time series. A jump component is introduced to absorb sporadic extreme values that may otherwise weaken the ability to detect slow-moving disease outbreaks. Our approach outperformed several state-of-the-art detection methods in terms of detection sensitivity using both simulated and real-world data.