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A binary prototype for time-series surveillance and intervention
Jason Olejarz1, Till Hoffmann2, Alex Zapf3
1Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
None:
Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, c, whenever the system is abnormal and no action is taken, or an immediate cost, k, with action, where k
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