Optimizing biosurveillance systems that use threshold-based event detection methods

We describe a methodology for optimizing a threshold detection-based biosurveillance system. The goal is to maximize the system-wide probability of detecting an ‘‘event of interest” against a noisy background, subject to a constraint on the expected number of false signals. We use nonlinear programming to appropriately set detection thresholds taking into account the probability of an event of interest occurring somewhere in the coverage area. Using this approach, public health officials can ‘‘tune” their biosurveillance systems to optimally detect various threats, thereby allowing practitioners to focus their public health surveillance activities. Given some distributional assumptions, we derive a one-dimensional optimization methodology that allows for the efficient optimization of very large systems. We demonstrate that optimizing a syndromic surveillance system can improve its performance by 20–40%. Published by Elsevier B.V.