The document discusses approaches for modern disease surveillance using collaboration and semantic web technologies. It describes how tools like InSTEDD Evolve use machine learning, social media, and geospatial data to improve early detection of disease outbreaks and facilitate effective coordination of public health responses. Key components of the proposed approach include automated analysis, user feedback loops, and representation of unstructured data to enable early detection and verification of health-related events.

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 Invited by Dr. Barbara Massoudi, PhD, MPH Lecture at Emory University Rollins School of Public Health Public Health Informatics, INFO 503 Atlanta, GA, USA

3. Background

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

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

13. Why location matters: Case Management Background

15. Why location matters: Case Management Background

17. Why location matters: Population Surveillance Background

19. Why location matters: Population Surveillance Background

22. 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

25. 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

26. 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

27. Evolve: Main Components Our Solution

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

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

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

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

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

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

38. Clustering: AGGLOMERATIVE Our Solution

39. Clustering: PARTITIONAL Our Solution

40. (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

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

43. Neural Network: Application Event? Our Solution

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

48. 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

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

50. 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

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

52. 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.

53. 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.

54. Acknowledgements

55. Through funding from:

57. BACKGROUND MATERIAL