1. Experiments and Results • Work Zip Experiment: Ideal situation in which work zip information for all patients is provided. Uses the spatial person model with the Home Zip node replaced by a Work Zip node. • Home Zip Experiment: Current situation in which home zip codes are used as an approximation to the point of infection. Uses the spatial person model. • Home Zip to Work Zip Experiment: Only home zip codes are provided but algorithm uses Home Zip to Work Zip model which permits the work zip code to be inferred. • All 3 experiments ran on 30 large scale simulated anthrax attacks of concentration 1.0 from the BARD simulator (Hogan et. el 2004). Spatial Person Model Home Zip to Work Zip Person Model Location of Release Angle of Release Home Zip Age Decile Exposed to Anthrax Time Of Release Gender Anthrax Infection Other ED Disease Respiratory from Anthrax Respiratory CC From Other Respiratory CC ED Admit from Other ED Admit from Anthrax Respiratory CC When Admitted ED Admission Work Zip Home Zip Location of Release Age Decile Exposed at Work Angle of Release Exposed at Home Gender Exposed to Anthrax Time Of Release Other ED Disease Anthrax Infection Respiratory from Anthrax Respiratory CC From Other Respiratory CC ED Admit from Anthrax ED Admit from Other Respiratory CC When Admitted ED Admission The Effect of Inferring Work Location from Home Location in Performing Bayesian Biosurveillance • 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 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. 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 Chris Garman1, Weng-Keen Wong2, Gregory Cooper1 1RODS Laboratory, University of Pittsburgh, 2School of Electrical Engineering and Computer Science, Oregon State University • 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 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). Anthrax Release Global nodes Location of Release Time of Release Angle of Release Interface nodes Person Model Person Model Person Model Person Model Person … models