Seamlessly integrating various early disease indicators with experts' opinion for better event warning and response...

1. Biosurveillance 2.0 Collaboration and Web 2.0/3.0 Semantic Technologies for Better Early Disease Warning and Effective Response Taha Kass-Hout Nicolás di Tada

2. Background

3. Late Detection and Response DAY CASES Opportunity for control Background

4. Early Detection and Response DAY CASES Opportunity for control Background

10. Traditional DISEASE SURVEILLANCE 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Detection algorithm “ What are we supposed to do with this?” Too many alerts The Problem

13. MODERN DISEASE SURVEILLANCE 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Feedback loop Our Approach Fewer and more actionable alerts Effective and coordinated response

14. Evolve: Main Components Feature extraction, reference and baseline information Tags Multiple Data Streams User-Generated and Machine Learning Metadata Comments Spatio-temporal Flags/Alerts/Bookmarks Evolve Bot Event Classification, Characterization and Detection Previous Event Training Data Previous Event Control Data Metadata extraction Machine learning Social network Professional feedback Anomaly detection Collaborative Spaces Hypotheses generationesting Our Solution

15. Evolve: Main Components Our Solution

16. Evolve: Process Item Hypothesis Field Actions and Verifications Feedback / Confirmation Our Solution Item Item Item Item Item Item Item Item

17. Advantages of Machine Learning P(malaria) = 22% P(influenza) = 13% P(other ILI) = 33% Our Solution

19. How to represent a document: cold fever Our Solution

21. Classifiers: Support Vector Machines (SVM) Our Solution

23. SVM – Non-linear? Φ : x -> φ ( x ) Map to higher-dimension space Our Solution

24. SVM – Filtering or classifying Classifier Document 1 Document 2 Document 3 Positives Negatives Training Document Training Document Our Solution

26. Clustering: AGGLOMERATIVE Our Solution

27. Clustering: PARTITIONAL Our Solution

28. (3) Bayesian Statistics Probability of disease A (flu) once symptom B (fever) is observed Probability of fever once flu is confirmed Probability of flu (prior or marginal) Probability of fever (prior or marginal) Our Solution

30. Neural Network: Structure Hidden Layer Output Layer Input Layer […] […] {I 0 ,I 1 ,……I n } {O 0 ,O 1 ,……O n } Weight Our Solution

31. Neural Network: Application Event? Our Solution

34. Genetic Algorithm: Model Fitness Fitness = 1/Area Our Solution

36. Genetic Algorithm: Process (4, 5 ,6, 3 ,5) (4,3,6,2,5) (5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2) (2,3,4,6,5) (3,4,5,2,6) (3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6) (4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 ) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 ) Our Solution

37. Result of incorporating all 5 techniques: Improved Surveillance Our Solution

38. Our Solution InSTEDD Evolve Related items (e.g., News articles) are grouped into a thread. Threads are later associated with events (hypothesized or confirmed). InSTEDD Evolve : ( http://instedd.org/evolve ) Tag cloud and semantic heatmap

39. Our Solution InSTEDD Evolve InSTEDD Evolve : ( http://instedd.org/evolve ) Filter feature which automatically filters for related items, updates the map and associated tags

40. Our Solution InSTEDD Evolve InSTEDD Evolve : ( http://instedd.org/evolve ) Auto-generated (machine-learning) tags. These tags are semantically ranked (a statistical probability match). Users can further train the classifier by accepting or rejecting a suggestion. Users can similarly train the geo-locator by simply accepting or rejecting and updating a location.

41. Our Solution InSTEDD Evolve InSTEDD Evolve : ( http://instedd.org/evolve ) Tracking the recent Avian Influenza Outbreak in Egypt (reports started to appear late January 2009). Notice the pattern of reported incidents along the Nile river.

42. Acknowledgements

43. Through funding from:

45. BACKGROUND MATERIAL