GarmanSS2005

Information about GarmanSS2005

Published on March 13, 2008

Author: Sabatini

Source: authorstream.com

Content

The Effect of Inferring Work Location from Home Location in Performing Bayesian Biosurveillance:  Conclusions On our large scale anthrax attack simulations, being able to infer the work zip appears to improve detection time over just using the home zip codes of the patients. Future work involves performing more experiments and running experiments over different sizes of simulated anthrax attacks. The Effect of Inferring Work Location from Home Location in Performing Bayesian Biosurveillance Chris Garman1, Weng-Keen Wong2, Gregory Cooper1 1RODS Laboratory, University of Pittsburgh, 2School of Electrical Engineering and Computer Science, Oregon State University Introduction Incorporating spatial information can improve the performance of outbreak detection algorithms (Buckeridge et. al. 2005) Spatial and spatio-temporal detection algorithms that monitor ED data typically use the patient’s home zip code as an approximation to the point of exposure If attack occurs at work, the work zip code is a much better approximation but the work zip code is often missing Related Work (Green and Kaufmann 2004) Locate significant clusters of increased morbidity in time and space using the patients’ home addresses Then finding the center of minimum distance to these clusters (Buckeridge 2005) Proposes two mobility models for estimating the probability of an individual being in a spatial unit given a time and home spatial unit: Workflow mobility model using workflow mobility data from US Census Non-workflow mobility model using estimates of travel probability and trip distance References Buckeridge DL. A method for evaluating outbreak detection in public health surveillance systems that use administrative data [Doctoral Dissertation]. Stanford, CA: Biomedical Informatics, Stanford University; 2005. Buckeridge, DL, Burkom H, Campbell M, Hogan WR, Moore AW. Algorithms for rapid outbreak detection: a research synthesis. Biomed Inform 2005; 38 (2):99-113. Cooper GF, Dash DH, Levander JD, Wong WK, Hogan WR, Wagner MM. Bayesian Biosurveillance of Disease Outbreaks. In Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence. Banff, Canada: AUAI Press; 2004. 94-103 p. Green MS, Kaufman Z. Syndromic surveillance for early location of bioterrorist incidents outside of residential areas. In Proceedings of the National Syndromic Surveillance Conference [CD-ROM]. Boston, MA: Fleetwood Multimedia, Inc.; 2004. Hogan WR, Cooper GF, Wallstrom, GL, Wagner MM. The bayesian aerosol release detector. In Proceedings of the National Syndromic Surveillance Conference [CD-ROM]. Boston, MA: Fleetwood Multimedia, Inc.; 2004. Methodology Estimate the probability P(Work Zip = X | Home Zip = Y) using historical data or census information Spatial detection algorithm now looks for a specific spatial pattern in the home locations and in the inferred work locations of the patients. This extension can be applied to any detection algorithm that uses a probabilistic approach to dealing with uncertainty We applied it to the Population-wide Anomaly Detection and Assessment (PANDA) algorithm (Cooper et. al. 2004) PANDA Models the effects of a large-scale airborne release of inhalational anthrax on the population using a causal Bayesian network Population-wide approach: each person in the population is represented as a subnetwork in the overall model Anthrax Release Person Model Global nodes Person Location of Release Time of Release Angle of Release Interface nodes models Person Model Person Model Person Model … Acknowledgements This research was supported by grants from the National Science Foundation (IIS-0325581), the Department of Homeland Security (F30602-01-2-0550), and the Pennsylvania Department of Health (ME-01-737).

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