Population-Wide Anomaly Detection

1. Population-Wide Anomaly Detection Weng-Keen Wong1, Gregory Cooper2, Denver Dash3, John Levander2, John Dowling2, Bill Hogan2, Michael Wagner2 1School of Electrical Engineering and Computer Science, Oregon State University, 2Realtime Outbreak and Disease Surveillance Laboratory, University of Pittsburgh, 3Intel Research, Santa Clara

2. Motivation • Suppose you monitor Emergency Department (ED) data which arrives in realtime • Can you specifically detect a large scale anthrax attack?

3. Model non-outbreak conditions and notice deviations Traditional Univariate Methods eg. Control chart, CUSUM, EWMA, time series models Spatial methods eg. Spatial Scan Statistic Multivariate methods eg. WSARE 2. Sat 2001-03-13: SCORE = -0.00000464 PVALUE = 0.00000000 12.42% ( 58/467) of today's cases have 20 ≤ Age < 30 AND Respiratory Syndrome = True 6.53% (653/10000) of baseline have 20 ≤ Age < 30 AND Respiratory Syndrome = True

4. Model non-outbreak conditions and notice deviations Traditional Univariate Methods eg. Control chart, CUSUM, EWMA, time series models Spatial methods eg. Spatial Scan Statistic These are non-specific methods – they look for anything unusual in the data but not specifically for the onset of an anthrax attack. Multivariate methods eg. WSARE 2. Sat 2001-03-13: SCORE = -0.00000464 PVALUE = 0.00000000 12.42% ( 58/467) of today's cases have 20 ≤ Age < 30 AND Respiratory Syndrome = True 6.53% (653/10000) of baseline have 20 ≤ Age < 30 AND Respiratory Syndrome = True

5. Population-wide ANomaly Detection and Assessment (PANDA) • A detector specifically for a large-scale outdoor release of inhalational anthrax • Uses a massive causal Bayesian network • Population-wide approach: each person in the population is represented as a subnetwork in the overall model

6. Population-Wide Approach • Note the conditional independence assumptions • Anthrax is infectious but non-contagious Anthrax Release Global nodes Interface nodes Location of Release Time of Release Each person in the population Person Model Person Model Person Model

7. Population-Wide Approach • Structure designed by expert judgment • Parameters obtained from census data, training data, and expert assessments informed by literature and experience Anthrax Release Global nodes Interface nodes Location of Release Time of Release Each person in the population Person Model Person Model Person Model

8. Person Model (Initial Prototype) Anthrax Release Time Of Release Location of Release … … Gender Age Decile Age Decile Gender Home Zip Home Zip Other ED Disease Other ED Disease Anthrax Infection Anthrax Infection Respiratory from Anthrax Respiratory CC From Other Respiratory from Anthrax Respiratory CC From Other Respiratory CC Respiratory CC ED Admit from Anthrax ED Admit from Other ED Admit from Anthrax ED Admit from Other Respiratory CC When Admitted Respiratory CC When Admitted ED Admission ED Admission

9. Person Model (Initial Prototype) Anthrax Release Time Of Release Location of Release … … Female 20-30 50-60 Male Gender Age Decile Age Decile Gender Home Zip Home Zip Other ED Disease Other ED Disease Anthrax Infection Anthrax Infection 15213 15146 Respiratory from Anthrax Respiratory CC From Other Respiratory from Anthrax Respiratory CC From Other Respiratory CC Respiratory CC ED Admit from Anthrax ED Admit from Other ED Admit from Anthrax ED Admit from Other Unknown False Respiratory CC When Admitted Respiratory CC When Admitted Yesterday ED Admission never ED Admission

10. Prototype is Computationally Feasible Aside from caching tricks, there are two main optimizations: • Incremental Updating • Equivalence Classes Performance: On P4 3.0 Ghz machine, 2 GB RAM, 45 seconds of initialization time, 3 seconds for each hour’s worth of ED data See Cooper G.F., Dash D.H., Levander J.D., Wong W-K, Hogan W. R., Wagner M. M. Bayesian Biosurveillance of Disease Outbreaks. In Proceedings of the 20th Conference on UAI. Banff, Canada: AUAI Press; 2004. pp94-103.

11. What do you gain with a population-wide approach? Coherent framework for: • Incorporating background knowledge • Incorporating different types of evidence • Data fusion • Explanation

12. 1. Incorporating Background Knowledge • Limited data from actual anthrax attacks available: • Postal attacks 2001 (Only 11 people affected, not representative of a large scale attack) • Sverdlovsk 1979 • But literature contains studies on the characteristics of inhalational anthrax

13. 1. Incorporating Background Knowledge • Can coherently incorporate different types of background knowledge eg. for inhalational anthrax: • Progression of symptoms • Incubation period • Spatial dispersion pattern

14. 1. Incorporating Background Knowledge • Can coherently incorporate different types of background knowledge eg. for inhalational anthrax: • Progression of symptoms • Incubation period • Spatial dispersion pattern At an individual level

15. 1. Incorporating Background Knowledge • Can coherently incorporate different types of background knowledge eg. for inhalational anthrax: • Progression of symptoms • Incubation period • Spatial dispersion pattern Can represent this by the effects over individuals

16. 2. Incorporating Evidence • Easily incorporate different types of evidence eg. spatial, temporal, demographic, symptomatic • Easily incorporate new evidence that distinguishes an individual (or individuals) from others • Modify the appropriate person model

17. 3. Data Fusion ED data OTC data • No data available during an actual anthrax attack that captures the correlation between these two data sources. • By modeling the actions of individuals, and incorporating background knowledge, we can come up with a plausible model of the effects of an attack on these two data sources.

18. 3. Data Fusion ED data OTC data ED data – individual patient records, available usually in real-time OTC data – aggregated over zipcode and available daily

19. 3. Data Fusion ED data OTC data By representing at the finest granularity (ie. each individual), we can easily deal with different spatial and temporal granularity in data fusion. See Wong, W-K, Cooper G.F., Dash D.H., Dowling, J.N., Levander J.D., Hogan W. R., Wagner M. M. Bayesian Biosurveillance Using Multiple Data Streams. In Proceedings of the 3rd National Syndromic Surveillance Conference, 2004.

20. Important to know why the model believes an anthrax attack is occurring Can find the subset of evidence E* that most influences such a belief In PANDA, E* would correspond to a group of individuals Identify the individuals that most contribute to the hypothesis of an attack 4. Explanation

21. 4. Explanation Currently, we identify the top equivalence classes that contribute the most to the hypothesis that an attack is occurring Can also use the Bayesian network to calculate the most likely location of release and time of release

22. Future Work • More sophisticated person models • Improved explanation capabilities • Validation of data fusion model • More disease models apart from anthrax • Contagious disease models • Combining outputs from multiple Bayesian detectors

23. Thank You! RODS Laboratory: http://rods.health.pitt.edu Bayesian Biosurveillance: http://www.cbmi.pitt.edu/panda/ This research was supported by grants IIS-0325581 from the National Science Foundation, F30602-01-2-0550 from the Department of Homeland Security, and ME-01-737 from the Pennsylvania Department of Health.