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AN ASYMPTOTICALLY OPTIMAL STRATEGY IN SEQUENTIAL CHANGE DETECTION AND IDENTIFICATION APPLIED TO PROBLEMS IN BIOSURVEILLANCE

We describe a statistical model of detecting and identifying an outbreak of bioagents. Based on a sequence of observations, one needs to detect the outbreak of disease and identify its cause. To maintain public safety, it is important to detect it at the earliest, and at the same time, identify its cause as accurately as possible in order to take the most appropriate countermeasures. This boils down to solving optimally the tradeoff between the detection delay cost and the false alarm and misidentification costs to the society. We focus on developing an early detection system of anthrax, which is considered to be the most likely weapon in biological warfare. Despite its importance, early detection of anthrax is difficult because its initial symptoms resemble a cold. The detection system therefore needs to differentiate anthrax from flu and other bioagents that cause similar symptoms. We propose a new sequential decision strategy which triggers an alarm when the posterior probability of a certain type of change exceeds some fixed threshold for the first time, show its asymptotic optimality under the Bayesian formulation, and verify the result numerically.