RIFF - A Social Network and Collaborative Platform For Public Health Disease Surveillance

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RIFF - A Social Network and Collaborative Platform For Public Health Disease Surveillance

Transcript of RIFF - A Social Network and Collaborative Platform For Public Health Disease Surveillance

  • 1. Photo credit: IRMA (Integrated Risk Management for Africa)

2. Taha Kass-Hout and Nicolas di Tada, Summer 2008, Washington, DC, USA. 3.

  • What is public health disease surveillance
    • Public health surveillance is the ongoing systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination of these data to those who need to know. The final link in the surveillance chain is the application of these data to prevention and control. A surveillance system includes a functional capacity for data collection, analysis, and dissemination linked to public health programs.
  • What is syndromic surveillance?
    • US CDC defines syndromic surveillance as surveillance using health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response.

Thacker, S.B., and Berkelman, R.L. "Public Health Surveillance in the United States." Epidemiology Reviews 10 (1988): 164-90. 4.

  • Current systems design, analysis and evaluation of disease surveillance systems has been geared towards specific data sources and detection algorithms not humans
    • Much less has been towards interaction with responders and domain experts across agencies and at multiple levels
    • Often provide contradictory interpretations of ongoing events
  • We have disease surveillance systems in place for those threats we have been faced with before
    • We are more vulnerable to those we know about, but have not faced on a major scale
    • Even more vulnerable to those that we dont know about


  • The likelihood of disasters and disease outbreaks is growing
    • According to a recent Oxfam report, there has been a four-fold increase in the annual number of natural disasters
    • 30 new infectious diseases identified since 1973
  • Potential impact is getting greater
    • Impact on health, economies & security
    • Capable of spreading faster than ever before

http://www.oxfam.org/en/policy/briefingpapers/bp108_climate_change_alarm_0711 6.

  • To address these challenges by adopting asocial and collaborative decision making approachin order tofacilitate
    • early characterization and identificationof potential health threats
    • theirverification, assessmentandinvestigation
    • in order to recommendmeasures (public health and others)to control them


  • Event-based -ad-hoc unstructured reports issued by formal or informal sources
  • Indicator-based - (number of cases, rates, proportion of strains)

Timeliness, Representativeness, Completeness, Predictive Value, Quality, Cost, Feasibility, 8. Identified risks Mandatory notification Laboratory surveillance Emerging risks Syndromic surveillance Mortality monitoring Healthcare activity monitoring Prescription monitoring Non healthcare based Veterinary surveillance Behavioral surveillance Environmental surveillance Poison centers Food safety/water supply Domestic Media NGOs Field Epi points

  • International
  • Distribution lists
    • ProMed (English, Chinese, Spanish, Russian, etc.)
  • International agencies
    • WHO
    • OIE
    • CDC
    • NASA (e.g., remote sensing, weather, population migration, bird migration, population density, plant, animal)
  • Confidential/Limited mailing list dissemination
    • ProMed (e.g., MBDS)
    • International health regulation agencies (WHO, OIE, CDC, NASA)
    • Threat bulletin (EWARN, ECDC)
  • Public dissemination
    • News, blogs, articles,
    • Health ministry press releases sites
    • Weekly releases (Eurosurveillance)

Adopted from WHO 9. Reduce Morbidity and Mortality and Improve Health Adopted from WHO 10. 1000Shigellainfections (100%) 50Shigellanotifications (5%)

  • Main attributes
    • Representativeness
    • Completeness
    • Predictive value positive

Specificity / Reliability Sensitivity / Timeliness Get as close to the bottom of the pyramid as possible Urge frequent reporting 11. Time

  • Main attributes
    • Timeliness

Analyze and interpretSignal as earlyas possible Automated analysis/thresholds 12.

  • Clickstream/Keyword Searching
  • Blogs/Chatrooms
  • News Sources
    • Local
    • National
    • International
  • Curated mailing lists (ProMED)
  • Multi-national surveillance (Eurosurveillance)
  • Validated official global alerts (WHO)

Sensitivity / Timeliness Specificity / Reliability

  • Main attributes
    • Data quality

13. Lab Confirmation Detection/ Reporting FirstCase Opportunityfor control Adopted from WHO Response DAY CASES 14. FirstCase Detection/ Reporting Confirmation Investigation Opportunityfor control Response DAY CASES Adopted from WHO 15. Nov 2002 Mar 2003 Progression of outbreak Electronic Surveillance Adopted from Brownstein, et al. Cases of atypical pneumoniaFoshan Nov 16th Infected Chinese Doctor Hong Kong hotel Feb 21st 305 Cases of acute resp Guangdong Province Feb 11th Pharma reportGuangdong Province November 27 Media reports Guangdong Province Feb 10 Astute physician on ProMED Feb 10 Initial WHO Report Feb 25 Official WHO Report March 10 16. 17. News articles Alerts Disease reports 18. 9/20, 15213, cough/cold, 9/21, 15207, antifever, 9/22, 15213, CC = cough, ... 1,000,000more records Huge mass of data Detection algorithm Too many alerts Duplicative and uni-directional channels Uncoordinated response 19.

  • Hybrid: Machine- and Human-based
  • Social, collaborative and cross-disciplinary
  • Web 2.0/3.0 platform


  • Better detection model
  • Better response model

Source:http://www.pbs.org/wgbh/pages/frontline/shows/georgia/outbreak/matrix.html Source:www.sociology.columbia.edu/pdf-files/bearmanarticle.pdf 21. News item 345 Field alerts Disease report Health News Field alerts News sources Alerts Data + Metadata

    • Collaboration and multi-directional communication between interested groups
    • Interactions beyond that allowed by original sources and with controlled visibility
    • Customizable, secure social and professional metadata around information

22. 9/20, 15213, cough/cold, 9/21, 15207, antifever, 9/22, 15213, CC = cough, ... 1,000,000more records Huge mass of data Feedback loop Fewer and more actionable alerts Effective and coordinated response Multi-directional communication 23. Feature extraction (including geo-location) Tags Comments Location Flags/Alerts/Bookmarks Environment Factors Animal Health Factors Remote Sensing Event Classification and Detection Previous Event Training Data Previous Event Control Data Metadata extraction Other reference information Machine learning Show event characterizations Social network Other inferred information Professional network feedback Professional feedback Anomaly detection Multiple data streams (multi-lingual) User-Generated and Machine Learning Metadata Existing Social Network (e.g., Comm. of interest) Riff Bot 24. 25. Kass-Hout and di Tada: Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual ISDS Conference, December 3-5, 2008 at the Raliegh Conference Civic Center.http://kasshout.blogspot.com/2008/12/best-poster-award-for-improving-public.htmlandhttp://www.isdsjournal.org/article/viewArticle/3308 26. Search: _____ {tag Cloud} Terms tagged by human collaborators or source {Event Tag cloud} XDiarreha XCholera XInfluenza XRespiratory lllness XFever [Show me unusual distributions] 27. 28. 29. Filters Item (e.g., disease report, news article, alert) summary and location (s) Tag cloud SubscriptionsSMS alerts Ratings, comments, alerts, flags Tags (automatic + humans classification) Thread (related Items) 30.


31. 32. 33. 34. 35. 36. Tracking the Avian Influenza Outbreak in Egypt (reports started to appear late January 2009). 37.

  • Current classifications (automated and corrected by human experts) includes:
    • 7 syndromes
    • 10 transmission modes
    • > 100 infectious diseases
    • > 180 micro-org