Optimizing Biosurveillance Systems that Use Threshold-based Event Detection Methods
Loading...
Authors
Fricker, Ronald D. Jr.
Banschbach, David
Subjects
Biosurveillance, Syndromic surveillance, Bioterrorism, Public health, Optimization, Shewhart chart
Advisors
Date of Issue
2012
Date
Publisher
Language
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%.
Type
Article
Description
Information Fusion, 13, 117-128.
The article of record as published may be found at http://dx.doi.org/10.1016/j.inffus.2009.12.002
The article of record as published may be found at http://dx.doi.org/10.1016/j.inffus.2009.12.002
Series/Report No
Department
Operations Research
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Fricker, R.D., Jr., and D. Banschbach (2012). Optimizing Biosurveillance Systems that Use Threshold-based Event Detection Methods, Information Fusion, 13, 117-128.
Distribution Statement
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.