Banschbach D. Optimizing biosurveillance systems that use threshold-based event detection methods. Inf Fusion 2012

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 non-linear 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, pub-lic 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 1-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 percent