Abstract
We present a method for automated detection of influenza epidemics. The method uses Hidden Markov Models with an Exponential-Gaussian mixture to characterize the non-epidemic and epidemic dynamics in a time series of influenza-like illness incidence rates. Our evaluation on real data shows a reduction in the number of false detections compared to previous approaches and increased robustness to variations in the data.
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Rath, T.M., Carreras, M., Sebastiani, P. (2003). Automated Detection of Influenza Epidemics with Hidden Markov Models. In: R. Berthold, M., Lenz, HJ., Bradley, E., Kruse, R., Borgelt, C. (eds) Advances in Intelligent Data Analysis V. IDA 2003. Lecture Notes in Computer Science, vol 2810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45231-7_48
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DOI: https://doi.org/10.1007/978-3-540-45231-7_48
Publisher Name: Springer, Berlin, Heidelberg
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