1.  2004 University of Pittsburgh Bayesian Biosurveillance Using Multiple Data Streams Greg Cooper, Weng-Keen Wong, Denver Dash*, John Levander, John Dowling, Bill Hogan, Mike Wagner RODS Laboratory, University of Pittsburgh * Intel Research, Santa Clara

2.  2004 University of Pittsburgh Outline 1.Introduction 2.Model 3.Inference 4.Conclusions

3.  2004 University of Pittsburgh 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

4.  2004 University of Pittsburgh ED Chief Complaint Data Being Collected by RODS Date / Time AdmittedAgeGenderHome ZipWork ZipChief Complaint Nov 1, 2004 3:0220-30Male15213Shortness of breath Nov 1, 2004 3:0970-80Female1513215213Fever :::::: Chief Complaint ED Records for Allegheny County

5.  2004 University of Pittsburgh Objective Using the ED and OTC data streams, detect a disease outbreak in a given region as quickly and accurately as possible

6.  2004 University of Pittsburgh Our Approach A detection algorithm that models each individual in the population Combines ED and OTC data streams The current prototype focuses on detecting an outdoor aerosolized release of an anthrax-like agent in Allegheny county Population-wide ANomaly Detection and Assessment (PANDA)

7.  2004 University of Pittsburgh PANDA Visit of Person to ED Location of Anthrax Release Anthrax Infection of Person Bayesian Network: A graphical model representing the joint probability distribution of a set of random variables Uses a causal Bayesian network Home Location of Person

8.  2004 University of Pittsburgh PANDA The arrows convey conditional independence relationships among the variables. They also represent causal relationships. Uses a causal Bayesian network Visit of Person to ED Location of Anthrax Release Anthrax Infection of Person Home Location of Person

9.  2004 University of Pittsburgh Outline 1.Introduction 2.Model 3.Inference 4.Conclusions

10.  2004 University of Pittsburgh A Schematic of the Generic PANDA Model for Non-Contagious Diseases Population Risk Factors Population Disease Exposure (PDE) Person Model Population-Wide Evidence Person Model

11.  2004 University of Pittsburgh A Special Case of the Generic Model Time of Release Person Model Anthrax Release Location of Release Person Model OTC Sales for Region Each person in the population is represented as a subnetwork in the overall model

12.  2004 University of Pittsburgh Location of Release Time Of Release Anthrax Infection Home Zip Respiratory from Anthrax Other ED Disease Gender Age Decile Respiratory CC From Other Respiratory CC Respiratory CC When Admitted ED Admit from Anthrax ED Admit from Other ED Acute Respiratory Infection Acute Respiratory Infection Daily OTC Purchase Last 3 Days OTC Purchase Non-ED Acute Respiratory Infection ED Admission The Person Model OTC Sales for Region

13.  2004 University of Pittsburgh Why Use a Population-Based Approach? 1.Representational power Spatial, temporal, demographic, and symptom knowledge of potential diseases 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 2.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.

14.  2004 University of Pittsburgh Location of Release Time Of Release Anthrax Infection Home Zip Respiratory from Anthrax Other ED Disease Gender Age Decile Respiratory CC From Other Respiratory CC Respiratory CC When Admitted ED Admit from Anthrax ED Admit from Other ED Acute Respiratory Infection Acute Respiratory Infection Daily OTC Purchase Last 3 Days OTC Purchase Non-ED Acute Respiratory Infection ED Admission The Person Model OTC Sales for Region

15. Location of Release Time Of Release Anthrax Infection Home Zip Respiratory from Anthrax Other ED Disease Gender Age Decile Respiratory CC From Other Respiratory CC Respiratory CC When Admitted ED Admit from Anthrax ED Admit from Other ED Acute Respiratory Infection Acute Respiratory Infection Daily OTC Purchase Last 3 Days OTC Purchase Non-ED Acute Respiratory Infection ED Admission The Person Model Age Decile GenderHome Zip Respiratory Chief Comp. Date Admitted 20-30Male15213YesToday Equivalence Class Example:

16.  2004 University of Pittsburgh Outline 1.Introduction 2.Model 3.Inference 4.Conclusions

17.  2004 University of Pittsburgh Inference Time of Release Person Model Anthrax Release Location of Release Person Model Derive P (Anthrax Release = true | OTC Sales Data & ED Data) OTC Sales for Region

18.  2004 University of Pittsburgh Inference AR = Anthrax ReleaseED = ED Data PDE = Population Disease ExposureOTC = OTC Counts P ( OTC, ED | PDE ) = P ( OTC | ED, PDE ) P ( ED | PDE ) Contribution of ED Data Contribution of OTC Counts Key Term in Deriving P ( AR | OTC, ED ) : 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.

19.  2004 University of Pittsburgh Inference AR = Anthrax ReleaseED = ED Data PDE = Population Disease ExposureOTC = OTC Counts P ( OTC, ED | PDE ) = P ( OTC | ED, PDE ) P ( ED | PDE ) The focus of the remainder of this talk Key Term in Deriving P ( AR | OTC, ED ) :

20.  2004 University of Pittsburgh Location of Release Time Of Release Anthrax Infection Home Zip Respiratory from Anthrax Other ED Disease Gender Age Decile Respiratory CC From Other Respiratory CC Respiratory CC When Admitted ED Admit from Anthrax ED Admit from Other ED Acute Respiratory Infection Acute Respiratory Infection Daily OTC Purchase Last 3 Days OTC Purchase Non-ED Acute Respiratory Infection ED Admission The Person Model OTC Sales for Region

21.  2004 University of Pittsburgh Incorporating the Counts of OTC Purchases Eq Class1 Zip1 OTC count Zip1 OTC count Eq Classs2 Zip1 OTC count Person1 Zip1 OTC count Person2 Zip1 OTC count Person3 Zip1 OTC count Person4 Zip1 OTC count Approximate binomial distribution with a normal distribution

22.  2004 University of Pittsburgh The PANDA OTC Model P (OTC sales = X | ED, PDE ) Recall that: P ( OTC, ED | PDE ) = P ( OTC | ED, PDE ) P ( ED | PDE )

23.  2004 University of Pittsburgh Example Age Decile GenderHome Zip Respiratory Chief Comp. Date Admitted 50-60Male15213YesToday Equivalence Class 1 ~ Normal(100,100)

24.  2004 University of Pittsburgh Example Age Decile GenderHome Zip Respiratory Chief Comp. Date Admitted 50-60Male15213YesToday Equivalence Class 1 ~ Normal(100,100) Age Decile GenderHome Zip Respiratory Chief Comp. Date Admitted 50-60Female15213YesToday Equivalence Class 2 ~ Normal(150,225)

25.  2004 University of Pittsburgh Example Age Decile GenderHome Zip Respiratory Chief Comp. Date Admitted 50-60Male15213YesToday Equivalence Class 1 ~ Normal(100,100) Age Decile GenderHome Zip Respiratory Chief Comp. Date Admitted 50-60Female15213YesToday 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)

26.  2004 University of Pittsburgh Example Now suppose 260 units are sold in the county P( OTC Sales = 260 | ED Data, PDE ) = Normal( 260; 250, 325 ) = 0.001231 260

27.  2004 University of Pittsburgh Inference Timing Machine: P4 3 Gigahertz, 2 GB RAM Initialization Time (seconds) Each hour of data (seconds) ED model555 ED and OTC model 2295

28.  2004 University of Pittsburgh A Current Limitation Problem: Currently we assume unrealistically that a person only makes OTC purchases in his or her home zip code Approach 1: Aggregate OTC-counts (e.g., at the county level) Approach 2: For each home zip code, model the distribution of zip codes where OTC purchases are made

29.  2004 University of Pittsburgh Outline 1.Introduction 2.Model 3.Inference 4.Conclusions

30.  2004 University of Pittsburgh 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

31.  2004 University of Pittsburgh Conclusions PANDA is a multivariate algorithm that can combine multiple data streams Modeling each individual in the population is computationally feasible (so far) An evaluation of the PANDA approach to modeling multiple data streams is in progress using semi-synthetic test data

32.  2004 University of Pittsburgh Thank you Current funding: National Science Foundation Department of Homeland Security Earlier funding: DARPA http://www.cbmi.pitt.edu/panda/ gfc@cbmi.pitt.edu

33.  2004 University of Pittsburgh

34. The PANDA OTC Model Model the OTC purchases for each Equivalence Class E i as a binomial Distribution. E i ~ Binomial(N E i,P E i )

35.  2004 University of Pittsburgh The PANDA OTC Model Model the OTC purchases for each Equivalence Class E i as a binomial Distribution. E i ~ Binomial(N E i,P E i ) Number of people in Equivalence Class E i Probability of an OTC cough medication purchase during the previous 3 days by each person in Equivalence Class E i

36.  2004 University of Pittsburgh The PANDA OTC Model Model the OTC purchases for each Equivalence Class E i as a binomial Distribution. Approximate the binomial distribution as a normal distribution. E i ~ Binominal(N E i,P E i )  Normal(  E i,  2 E i )

37.  2004 University of Pittsburgh The PANDA OTC Model Model the OTC purchases for each Equivalence Class E i as a binomial Distribution. Approximate the binomial distribution as a normal distribution. E i ~ Binominal(N E i,P E i )  Normal(  E i,  2 E i )  E i = N E i × P E i  2 E i = N E i × P E i × (1 - P E i )

38.  2004 University of Pittsburgh Computational Cost of a Population-Wide Approach? ~1.4 million people in Allegheny County, Pennsylvania

39.  2004 University of Pittsburgh Equivalence Classes The ~1.4M people in the modeled population can be partitioned into approximately 24,240 equivalence classes