1. Bayesian Biosurveillance Gregory F. Cooper Center for Biomedical Informatics University of Pittsburgh gfc@cbmi.pitt.edu The research described in this talk is based on collaborative work with members of the Bayesian Biosurveillance project and the RODS Laboratory at the University of Pittsburgh, and the Auton Laboratory at Carnegie Mellon University. Special thanks Bill Hogan for the BARD slides that are included in this presentation.

2. Outline Provide a brief overview of Bayesian inference as applied to outbreak detection Show an example of a Bayesian biosurveillance algorithm

3. Biosurveillance Definition: Biosurveillance is the process of monitoring for new outbreaks of infectious disease Goal: Detect an infectious disease outbreak in a population rapidly and accurately

4. Bayes Rule

8. parameter prior hypothesis prior

9. Bayes Rule for Outbreak Detection One hypothesis is that there is no disease outbreak at the present time. Other hypothesis postulates various types of outbreaks, such as anthrax, small pox, plague, and many others.

10. Some Advantages of a Bayesian Approach to Biosurveillance Permits specification of prior knowledge and belief –Knowledge about outbreak diseases –Belief about whether, when and how an outbreak will occur, based on experience, intel, and intelligent guesses. Facilitates modeling –of complex outbreaks –with multiple data streams Yields inferences –of P(outbreak | data), which can be used directly in a decision analysis about what to do –of other statistics of interest, such as the expected number of people infected in a probable outbreak situation

11. An Example of a Bayesian Biosurveillance Algorithm BARD (Bayesian Aerosol Release Detector) is an outbreak detection system that is designed to compute the posterior probability of an outdoor, windborne release of anthrax spores Outbreak data –Emergency Dept (ED) chief complaints –OTC –BioWatch sensors Additional data –Weather data –Dispersion data

12. BARD: Overview Seeks earlier, more sensitive detection of windborne outbreaks through recognition of a characteristic dispersion pattern An alert not only detects outbreak, but characterizes it as windborne Derives estimates of release location, quantity and timing Has been running in Pittsburgh (since 1/2005) and Philadelphia (since 6/2005)

13. Typical Computation for Aerosol Releases: Predict Consequences of Release Parameters Weather Quantity Released Location of Release Dispersion Model Downwind airborne concentrations Model of Effects of Aerosol on People Predicted effect on biosurveillance data over time Time of Release

14. Weather Quantity released Location of release Inversion of Dispersion Model Downwind airborne concentrations Inversion of Model of Aerosol Effects on Biosurveillance Data Observed effect on biosurveillance data over time Time of Release BARD Uses Bayesian Inference to Derive Release Parameters from Data

15. P(Data | Release Params) is very low P(Data | Release Params) is relatively high Wind direction 2 days agoWind direction 3 days ago BARD Searches for the Optimal Release Parameters

16. The Structure of the BARD Model

17. Dispersion Model

18. The Gaussian Plume Model where d is the number of spores inhaled by an individual Q is the number of kilograms of spores released w is the number of spores per kilogram V E is minute ventilation (x, y, h) is the coordinate of the hypothesized release location where x and y specify the location on the surface of the earth and h specifies height above ground (x i y i, h i ) is similarly the coordinate of the patient’s location  x and  Z are the distributions of spores in the crosswind direction u is the wind speed s is the atmospheric stability

19. The Structure of the BARD Model Model of effects on an person

20. BARD Evaluation: Methods Used BARD to generate data for 20 simulated windborne anthrax releases (Thus, this is a preliminary evaluation.) Injected that ED respiratory chief complaint data into a real historical dataset Used historical weather data for simulation and detection

22. BARD Evaluation: Results Sensitivity = 100% at false alarm rate of zero (for detection within seven days of the simulated release) Mean timeliness at false alarm rate of zero: –From time of release, 3.1 days –From time of first ED visit, 1.2 days (28 hours) Mean accuracy of release parameters output by BARD: –X coordinate of release location: 3,400 meters –Y coordinate of release location: 84 meters –Height of release: 124 meters –Quantity of release: 0.5 kilograms –Time of release: 0.008 days

23. Accuracy of Identified Release Parameters

24. BARD: Search Time ~ 3 minutes to consider 200,000 release scenarios in searching for an outbreak in the Pittsburgh metropolitan area

25. Summary Bayesian biosurveillance has a number of attractive qualities has been implemented in several algorithms is practical has many unexplored, promising directions for future work

26. Acknowledgments This research was supported by the National Science Foundation, the Pennsylvania Department of Health, the Department of Homeland Security, DARPA, and the Centers for Disease Control and Prevention.

27. Additional Information Bayesian Biosurveillance Project: www.cbmi.pitt.edu/panda Real-Time Outbreak and Disease Surveillance (RODS) Laboratory: rods.health.pitt.edu Greg Cooper: gfc@cbmi.pitt.edu