1. Syndromic Surveillance in Montreal: An Overview of Practice and Research David Buckeridge, MD PhD Epidemiology and Biostatistics, McGill University Surveillance Team, Montreal Public Health QPHI Surveillance Meeting KFL&A Public Health, Kingston, ON June 13 th, 2008

2. Syndromic Surveillance in Montreal (ou, Vigie Multirisque) Population Under Surveillance Intervention Decision Intervention Guidelines Public Health Action 3. Conveying information for action Population Pattern Definitions Pattern Report Pattern Detection Algorithm 2. Detecting population patterns Event Reports Individual Event Definitions Event Detection Algorithm Data Describing Population 1. Identifying individual cases Decision Algorithm Knowledge Telehealth 911 Calls Hospital Reportable Telehealth 911 Calls Hospital Reportable Counts, Native coding schemes, ISDS consensus syndromes Routine SaTScan, alerts for shared addresses Daily review of analysis results, not clear protocol

3. Vigie Multirisque: Data Sources  Emergency Departments  Currently: All 22 ED in Montreal via web form, total counts, no diagnosis or chief complaint  Future: Automated feeds under development, triage code and level, chief complaint, postal code  EMS Dispatch and Billing  Long-Term Care  Tele Health  Reportable Diseases

4. Vigie Multirisque: Dashboard

5. Vigie Multirisque: Analysis

7. Vigie Multirisque: Descriptive

8. Surveillance Research

9. Syndromic Surveillance Research Population Under Surveillance Intervention Decision Intervention Guidelines Public Health Action 3. Conveying information for action Population Pattern Definitions Pattern Report Pattern Detection Algorithm 2. Detecting population patterns Event Reports Individual Event Definitions Event Detection Algorithm Data Describing Population 1. Identifying individual cases Decision Algorithm Knowledge Subsets of admin data for ILI surveillance

10. Looking for the Leading ILI Indicator in Billing Data

11. Syndromic Surveillance Research Population Under Surveillance Intervention Decision Intervention Guidelines Public Health Action 3. Conveying information for action Population Pattern Definitions Pattern Report Pattern Detection Algorithm 2. Detecting population patterns Event Reports Individual Event Definitions Event Detection Algorithm Data Describing Population 1. Identifying individual cases Decision Algorithm Knowledge Subsets of admin data for ILI surveillance Accuracy of ICD codes and syndromes in ambulatory practice

12. Syndromic Surveillance Research Population Under Surveillance Intervention Decision Intervention Guidelines Public Health Action 3. Conveying information for action Population Pattern Definitions Pattern Report Pattern Detection Algorithm 2. Detecting population patterns Event Reports Individual Event Definitions Event Detection Algorithm Data Describing Population 1. Identifying individual cases Decision Algorithm Knowledge Subsets of admin data for ILI surveillance Accuracy of ICD codes and syndromes in ambulatory practice 1. Selecting the best algorithm 2. 3. 1. Selecting the best algorithm 2. 3.

13. Building the Knowledge-Base for Algorithm Selection 1. Model the aberrancy detection process 2. Evaluate modeled algorithms using high throughput software 3. Use machine learning to identify and model the determinants of detection

14. Syndromic Surveillance Research Population Under Surveillance Intervention Decision Intervention Guidelines Public Health Action 3. Conveying information for action Population Pattern Definitions Pattern Report Pattern Detection Algorithm 2. Detecting population patterns Event Reports Individual Event Definitions Event Detection Algorithm Data Describing Population 1. Identifying individual cases Decision Algorithm Knowledge Subsets of admin data for ILI surveillance Accuracy of ICD codes and syndromes in ambulatory practice 1. Selecting the best algorithm 2. Looking for connected cases 3. 1. Selecting the best algorithm 2. Looking for connected cases 3.

15. System Architecture Python, R-Server, SaTScan Statistical Analysis Server PostGreSQL / PostGIS DB Spatial Database Apache + PHP, MapServer + MapScript Mapping and Web Server Web Client Firefox, Explorer Current Case Management System DCIMI Database DCIMI Client Oracle Oracle Forms Web-based Cartography Software

16. Organizing Data by Person, Place and Time PostGreSQL / PostGIS DB Spatial Database Person MADO Name Birthdate … Place Address X, Y Place Type (Residence, Workplace) … Episode Onset Date Disease Type … Contact Situation Role (Home, Work, School, …) Active Date …

17. Address Validation and Correction in a Public Health System

18. Dracones – Query Form Person Time Place

20. Dracones – SaTScan Results

21. Syndromic Surveillance Research Population Under Surveillance Intervention Decision Intervention Guidelines Public Health Action 3. Conveying information for action Population Pattern Definitions Pattern Report Pattern Detection Algorithm 2. Detecting population patterns Event Reports Individual Event Definitions Event Detection Algorithm Data Describing Population 1. Identifying individual cases Decision Algorithm Knowledge Optimal decision making after an alarm Subsets of admin data for ILI surveillance Accuracy of ICD codes and syndromes in ambulatory practice 1. Selecting the best algorithm 2. Looking for connected cases 3. Spatial TB clusters 1. Selecting the best algorithm 2. Looking for connected cases 3. Spatial TB clusters

22. Using Surveillance Information to Manage Outbreaks Effectively  Much research on the statistical accuracy of aberrancy detection algorithms  Little attention to what happens next  Some attempts to describe response protocols (e.g., flow chart, wait a day)  No quantitative modeling of response  Rational response is important  Small window to obtain benefit  Surveillance information uncertain

23. The Traditional Surveillance Alert Response Model Environmental Data Knowledge Detection Method Investigat e Confirm Wait Review Records No Outbreak No Outbreak Alert No Alert Intervention No Intervention Yes No

24. Identifying an Optimal Policy  The goal is to identify a policy, or a mapping from a belief state (probability distribution over states) to actions  The belief state, provides the same information as maintaining the complete history  Value iteration is used to solve POMDP

25. Applying a POMDP to Surveillance S - True outbreak state {No Outbreak, D1, ….} O - Output from detection algorithm {0,1} A - Possible public health actions T(s,a,s’) - Impact of actions given the state R(s,a) - Costs of actions and outbreak states Do nothing Review records Investigate cases Declare outbreak ActionTransition (Izadi M & Buckeridge DL, 2007)

26. POMDP Policy Dominates Ad Hoc Policy

27. Syndromic Surveillance Research Population Under Surveillance Intervention Decision Intervention Guidelines Public Health Action 3. Conveying information for action Population Pattern Definitions Pattern Report Pattern Detection Algorithm 2. Detecting population patterns Event Reports Individual Event Definitions Event Detection Algorithm Data Describing Population 1. Identifying individual cases Decision Algorithm Knowledge Optimal decision making after an alarm Evaluating Syndromic Surveillance in Public Health Practice: Detecting Waterborne Outbreaks Subsets of admin data for ILI surveillance Accuracy of ICD codes and syndromes in ambulatory practice 1. Selecting the best algorithm 2. Looking for connected cases 3. Spatial TB clusters 1. Selecting the best algorithm 2. Looking for connected cases 3. Spatial TB clusters

28. Automated and ‘Traditional’ Surveillance for Waterborne Outbreaks Dispersion Exposure Latent Infected Infectious (Symptom atic) Infectious (Asympto matic) Tele- health ED Out- patient Stool Test Analysis by Public Health DiseaseHealth Care UtilizationReportable Disease Surveillance Syndromic Surveillance Analysis by Public Health Outbreak Detection Historical Tele- health and ED Data Historical Case Reports O S S S R R R R R OO S,R

29. Modeling Dispersion of Microorganisms Dispersion

30. Modeling Infection: Mobility

31. Mobility-Weighted Infection Probability by Home Address

32. Modeling Disease, Visits, Testing, Reporting to Public Health

33. Evaluating the Effect of Surveillance Enhancements

34. For more information…