Optimizing Biosurveillance Systems that Use Threshold-based Event Detection Methods
dc.contributor.author | Fricker, Ronald D. Jr. | |
dc.contributor.author | Banschbach, David | |
dc.date.accessioned | 2014-02-06T00:06:57Z | |
dc.date.available | 2014-02-06T00:06:57Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Fricker, R.D., Jr., and D. Banschbach (2012). Optimizing Biosurveillance Systems that Use Threshold-based Event Detection Methods, Information Fusion, 13, 117-128. | en_US |
dc.identifier.uri | https://hdl.handle.net/10945/38730 | |
dc.description | Information Fusion, 13, 117-128. | en_US |
dc.description | The article of record as published may be found at http://dx.doi.org/10.1016/j.inffus.2009.12.002 | en_US |
dc.description.abstract | 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%. | en_US |
dc.rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. | en_US |
dc.title | Optimizing Biosurveillance Systems that Use Threshold-based Event Detection Methods | en_US |
dc.type | Article | en_US |
dc.contributor.department | Operations Research | en_US |
dc.subject.author | Biosurveillance, Syndromic surveillance, Bioterrorism, Public health, Optimization, Shewhart chart | en_US |