Bayesian Biosurveillance Using Multiple Data Streams

1. Bayesian Biosurveillance Using Multiple Data Streams Weng-Keen Wong, Greg Cooper, Denver Dash*, John Levander, John Dowling, Bill Hogan, Mike Wagner RODS Laboratory, University of Pittsburgh *Intel Research This research was supported in part by grants from the National Science Foundation (IIS-0325581), the Defense Advanced Research Projects Agency (F30602-01-2-0550), and the Pennsylvania Department of Health (ME-01-737).

2. Over-the-Counter (OTC) Data Being Collected by the National Retail Data Monitor (NRDM) 19,000 stores 50% market share nationally >70% market share in large cities

3. ED Chief Complaint Data Being Collected by RODS Chief Complaint ED Records for Allegheny County

4. Objective Using the ED and OTC data streams, detect a disease outbreak in a given region as quickly and accurately as possible

5. Our Approach • A unique detection algorithm that models each individual in the population • Combines ED and OTC data streams • Focuses on detecting an outdoor aerosolized release of an anthrax-like agent in Allegheny county Population-wideANomalyDetectionandAssessment (PANDA)

6. PANDA: Population-wide Anomaly Detection and Assessment Uses a causal Bayesian network Home Location of Person Visit of Person to ED Anthrax Infection of Person Location of Anthrax Release Bayesian Network: A graphical model representing the joint probability distribution of a set of random variables

7. PANDA: Population-wide Anomaly Detection and Assessment Uses a causal Bayesian network Home Location of Person Visit of Person to ED Anthrax Infection of Person Location of Anthrax Release The arrows convey conditional independence relationships among the variables. They also represent causal relationships.

8. Outline • Introduction • Model • Inference • Conclusions

9. The Generic PANDA Model for Non-Contagious Diseases Population Risk Factors Population Disease Exposure (PDE) Person Model Person Model Person Model Person Model Population-Wide Evidence

10. A Special Case of the Generic Model Anthrax Release Location of Release Time of Release Person Model Person Model Person Model Person Model OTC Sales for Region Each person in the population is represented as a subnetwork in the overall model

11. The Person Model Location of Release Age Decile Home Zip Time Of Release Gender Anthrax Infection Other ED Disease Non-ED Acute Respiratory Infection Respiratory from Anthrax Respiratory CC From Other ED Acute Respiratory Infection Acute Respiratory Infection Respiratory CC ED Admit from Anthrax ED Admit from Other Daily OTC Purchase Respiratory CC When Admitted Last 3 Days OTC Purchase ED Admission OTC Sales for Region

12. Why Population Based? • Representational power • Background knowledge about spatial, temporal, demographic, and symptom information can be coherently represented in a single model • Spatial, temporal, demographic, and symptom evidence can be combined to derive a posterior probability of a disease outbreak • Representational flexibility New types of knowledge and evidence can be readily incorporated into the model Hypothesis: A population-based approach will achieve better detection performance than non-population-based approaches.

13. Computational Cost of a Population-Wide Approach? ~1.4 million people in Allegheny County, Pennsylvania

14. Equivalence Classes The ~1.4M people in the modeled population can be partitioned into approximately 24,240 equivalence classes

15. The Person Model Location of Release Age Decile Home Zip Time Of Release Gender Anthrax Infection Other ED Disease Non-ED Acute Respiratory Infection Respiratory from Anthrax Respiratory CC From Other ED Acute Respiratory Infection Acute Respiratory Infection Respiratory CC ED Admit from Anthrax ED Admit from Other Daily OTC Purchase Respiratory CC When Admitted Last 3 Days OTC Purchase ED Admission OTC Sales for Region

16. The Person Model Location of Release Age Decile Home Zip Time Of Release Gender Anthrax Infection Other ED Disease Non-ED Acute Respiratory Infection Respiratory from Anthrax Respiratory CC From Other ED Acute Respiratory Infection Acute Respiratory Infection Respiratory CC ED Admit from Anthrax ED Admit from Other Daily OTC Purchase Respiratory CC When Admitted Last 3 Days OTC Purchase ED Admission Equivalence Class Example:

17. Outline • Introduction • Model • Inference • Conclusions

18. Inference Anthrax Release Location of Release Time of Release Person Model Person Model Person Model Person Model OTC Sales for Region Derive P (AnthraxRelease = true | OTC Sales Data & ED Data)

19. Inference Key Term in Deriving P ( AR|OTC, ED ) : P ( OTC, ED | PDE ) = P ( OTC | ED, PDE ) P ( ED | PDE ) Contribution of ED Data Contribution of OTC Counts Details in: Cooper GF, Dash DH, Levander J, Wong W-K, Hogan W, Wagner M. Bayesian Biosurveillance of Disease Outbreaks. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2004.

20. Inference Key Term in Deriving P ( AR | OTC, ED ) : P ( OTC, ED | PDE ) = P ( OTC | ED, PDE ) P ( ED | PDE ) The focus of the remainder of this talk

21. The PANDA OTC Model Model the OTC purchases for each Equivalence Class Ei as a binomial Distribution. Ei ~ Binomial(NEi ,PEi)

22. The PANDA OTC Model Model the OTC purchases for each Equivalence Class Ei as a binomial Distribution. Ei ~ Binomial(NEi ,PEi) Number of people in Equivalence Class Ei Probability of an OTC cough medication purchase during the previous 3 days by each person in Equivalence Class Ei

23. The PANDA OTC Model Model the OTC purchases for each Equivalence Class Ei as a binomial Distribution. Approximate the binomial distribution as a normal distribution. Ei ~ Binominal(NEi ,PEi)  Normal(Ei,2Ei)

24. The PANDA OTC Model Model the OTC purchases for each Equivalence Class Ei as a binomial Distribution. Approximate the binomial distribution as a normal distribution. Ei ~ Binominal(NEi ,PEi)  Normal(Ei,2Ei) Ei = NEi × PEi 2Ei = NEi×PEi× (1 - PEi)

25. The PANDA OTC Model P (OTCsales = X | ED, PDE ) Recall that: P ( OTC, ED | PDE ) = P ( OTC | ED, PDE ) P ( ED | PDE )

26. Example Equivalence Class 1 ~ Normal(100,100)

27. Example Equivalence Class 1 ~ Normal(100,100) Equivalence Class 2 ~ Normal(150,225)

28. Example Equivalence Class 1 ~ Normal(100,100) Equivalence Class 2 ~ Normal(150,225) If these were the only 2 Equivalence Classes in the County then County Cough & Cold OTC ~ Normal(100+150,100+225)

29. Example Now suppose 260 units are sold in the county P( OTC Sales = 260 | ED Data, PDE ) = Normal( 260; 250, 325 ) = 0.001231 260

30. Inference Timing Machine: P4 3 Gigahertz, 2 GB RAM

31. Outline • Introduction • Model • Inference • Conclusions

32. Challenges in Population-Wide Modeling Include … • Obtaining good parameter estimates to use in modeling (e.g., the probability of an OTC cough medication purchase given an acute respiratory illness) • Modeling time and space in a way that is both useful and computationally tractable • Modeling contagious diseases

33. Conclusions • PANDA is a multivariate algorithm that can combine multiple data streams • Modeling each individual in the population is computationally feasible • An evaluation of this approach using simulations is in progress

34. Thank you http://www.cbmi.pitt.edu/panda/