Seminar talk at the University of Twente, presenting preliminary infoveillance results, using Twitter and Google

1. Associate Professor Department of Health Policy, Management and Evaluation, University of Toronto; Senior Scientist , Centre for Global eHealth Innovation, Division of Medical Decision Making and Health Care Research; Toronto General Research Institute of the UHN, Toronto General Hospital, Canada [email_address] Gunther Eysenbach MD MPH Gunther Eysenbach MD MPH Infodemiology and Infoveillance Infodemiology and Infoveillance

2. CDC http://www.webcitation.org/5h4wOBpC1 Swine Flu / H1N1 Posible caso de influenza, (C) Hello32020, licensed under CC-by license

3. Count what is countable, measure what is measurable. What is not measurable, make measurable. [Galileo Galilei]

5. “ Infodemiology” the epidemiology of information Describing and analyzing health information & communication patterns (e.g. on the Web) for public health purposes Demand Metrics (search & navigation) Supply Metrics (what’s published) Gunther Eysenbach J Med Internet Res 2009;11(1):e11

6. “ Infodemiology” the epidemiology of information Describing and analyzing information & communication patterns and its relationship to population health status The science of distribution and determinants of disease in populations Epidemiology Public Health Professionals Policy Makers Public Health Interventions Policy Decisions Population Health Status The notion of “infodemiology” G. Eysenbach. Infodemiology. American Journal of Medicine , 2002;113(0):763-765 Information & Communication patterns

7. Infoveillance = “using infodemiology data for surveillance purposes” Eysenbach G Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet J Med Internet Res 2009;11(1):e11 URL: http://www.jmir.org/2009/1/e11 doi: 10.2196/jmir.1157

8. Infovigil - an infoveillance prototype Centre for Global eHealth Innovation, Toronto, in collaboration with OAHPP Infovigil Aggregator/ Datamining/ Vizualisation Public, Clinicians, Epidemiologists Websites Filter Keywords / Concepts of Interest Online Questionnaires

9. http://www.jmir.org/2009/1/e11/

11. “ Infodemiology” the epidemiology of information Describing and analyzing health information & communication patterns (e.g. on the Web) Demand Metrics Supply Metrics Gunther Eysenbach Infodemiology: the epidemiology of (mis)information American Journal of Medicine , 2002;113(0):763-765

12. Information prevalence (plotted against disease incidence)

13. Information occurance ratios: Linguistic/cultural differences and inequalities in health information availability

14. Co-occurance ratios as metric for knowledge translation/uptake

16. http://www.webcitation.org/5h43hTpxz

17. Swineflu vs H1N1 terminology on Twitter

18. “ Infodemiology” the epidemiology of information Describing and analyzing health information & communication patterns (e.g. on the Web) Demand Metrics Supply Metrics Gunther Eysenbach Infodemiology: the epidemiology of (mis)information American Journal of Medicine , 2002;113(0):763-765

23. A “demand metric”: Health-related searches on the web Eysenbach G, Köhler C. What is the Prevalence of Health-related Searches on the World Wide Web? Qualitative and Quantitative Analysis of Search Engine Queries on the Internet. Proc AMIA Annu Fall Symp ; 2003: 225-229 Eysenbach G, Köhler C. Health-Related Searches on the Internet JAMA , Jun 2004; 291: 2946. Method also used by e.g.: Cobb NK, Graham AL Characterizing Internet Searchers of Smoking Cessation Information J Med Internet Res 2006;8(3):e17 <URL: http://www.jmir.org/2006/3/e17/>

24. Breakdown of health-related search engine queries by category Eysenbach G, Köhler C. Health-Related Searches on the Internet JAMA 2004; 291:2946

25. Daily searches on Google.ca for “flu” or “flu symptoms” 2003/2004 2007/2008 2004/2005 2005/2006 2006/2007 Impressions (searches)

27. “ before diagnoses are confirmed” In reality, many syndromic surveillance systems tap into clinical data - we know little what is going on at people’s home when they first notice symptoms, before they see/call a doctor

28. “ I have an idea -- let’s start thinking outside the box”

29. What is the first thing many people do before they see a doctor (or call Telehealth Ontario), or take OTC medicines?

30. Before seeing/calling a doctor many people do this:

31. Confirmed diagnoses Preliminary diagnoses Orders Laboratory tests Physician office visits EMS activity ED visits, Hospitalizations Incubation Period Exposure Symptom onset Health behavior Information seeking (web clickstream, etc.) Over the counter and prescription medications School and work absenteeism Nurse triage telephone calls Health and healthcare behaviors Healthcare encounter Surveillance of citizen self-assessments & status updates Initial findings Data types Timeline Biological sensors Syndromic Surveillance Diagnostics Final diagnosis Medical evaluation Additional evaluation Adapted from Mandl 2007

32. Two examples Demand-based infoveillance: Google Supply-based infoveillance: Twitter

33. Demand-based infoveillance Google searches

34. Infodemiology: Tracking demand for health information for syndromic surveillance Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

36. Correlation clicks vs next wk cases Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

37. Correlation sentinel physicians ILI reports vs next wk cases Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

38. Eysenbach G. Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet. J Med Internet Res 2009;11(1):e11 URL: http://www.jmir.org/2009/1/e11 doi: 10.2196/jmir.1157

39. J Ginsberg et al. Nature 2009 doi:10.1038/nature07634 A comparison of model estimates for the mid-Atlantic region (black) against CDC-reported ILI percentages (red), including points over which the model was fit and validated.

40. Wilson K, Brownstein JS. Early detection of disease outbreaks using the Internet. CMAJ 2009. DOI: 10.1503/cmaj.1090215

41. Supply-based infoveillance Example: Twitter status updates

55. The next step: Involving consumers, guiding consumer actively to questionnaires to solicit additional information

59. Syndromic Surveillance 2.0 Actively involving the public

68. “ Your proposal is innovative. Unfortunately, we won’t be able to use it because we never tried it’s something before.”

70. Thanks for your attention !

71. Associate Professor Department of Health Policy, Management and Evaluation, University of Toronto; Senior Scientist , Centre for Global eHealth Innovation, Division of Medical Decision Making and Health Care Research; Toronto General Research Institute of the UHN, Toronto General Hospital, Canada [email_address] www.jmir.org Twitter:eysenbach Gunther Eysenbach MD MPH Gunther Eysenbach MD MPH Thank you! Thank you !