Bayesian Disease Outbreak Detection that Includes a Model of Unknown Diseases

1. Bayesian Disease Outbreak Detection that Includes a Model of Unknown Diseases Yanna Shen and Gregory F. Cooper Intelligent Systems Program and Department of Biomedical Informatics University of Pittsburgh

2. Introduction • Outbreak detection algorithms: • Specific detection algorithms • Look for pre-defined anomalous pattern in the data • Non-specific detection algorithms • Try to detect any anomalous events, relative to some baseline of “normal” behavior

3. Safety-net detection approaches • Our safety-net algorithm: • A hybrid method that combines the specific and non-specific detection approaches • Detect known causes of anomalies well while having the non-specific approach serve as a “safety-net” • Bayesian approach • Operate on a time series of Emergency Department (ED) patient symptoms such as cough, fever and diarrhea

4. The population-wide disease model outbreak disease in population fraction person_1 disease person_2 disease person_N disease . . . person_1 evidence person_2 evidence person_N evidence . . .

5. An example population-wide disease model outbreak disease in population fraction person_1 disease person_2 disease person_N disease . . . person_1 cough state person_2 cough state person_N cough state . . .

6. Inference pop_dx outbreak disease in population fraction • Derive the posterior probability P(pop_dx | data) • Derive P(data | pop_dx) • Time complexity is exponential in NE (number of people who come to the ED) • Adapted the inference method given in (Cooper 1995), which performs inference that is polynomial in NE . . . person_1 disease person_2 disease person_n disease P(cough | disease state) = pu , where pu ~ Beta(αu ,βu) person_1 cough state person_2 cough state person_N cough state . . . data

7. Creating the datasets • Create a background time series: • Simulate the number of people who came to the ED on a given day without any disease outbreak • Simulate the cough status for each of these people • Create the outbreak cases by using FLOO (Neill 2005) • Overlay the outbreak cases onto the simulated background cases

8. Experimental setup 1 • Let du and dv be two CDC Category A diseases and du≠ dv A1 B1 Model: Test data:

9. Result (A1 vs. B1) • Plots showing the AMOC performances for experiment A1 and B1

10. Experimental setup 2 A2 B2 Model: Test data:

11. Result (A2 vs. B2) • Plots showing the AMOC performances for experiment A2 and B2

12. Summary • Introduced a Bayesian method for detecting disease outbreaks that combines a specific detection method with a non-specific method • Provided support that this hybrid approach helps detect unexpected disease more than it interferes with detecting unknown diseases

13. Future work • Explore distributions other than the uniform distribution for a disease symptom, such as cough, for the safety-net disease • Extend the model to consider multiple person evidences

14. Acknowledgements • This research was funded by a grant from the National Science Foundation (NSF IIS-0325581) • We thank the colleagues from the Department of Biomedical Informatics, the University of Pittsburgh, for their helpful comments on this work. • Wendy Chapman • John Dowling • John Levander • Melissa Saul • Garrick Wallstrom