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Evolve: InSTEDD's Global Early Warning and Response System

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American Medical Informatics Association (AMIA) Spring Congress, Walt Disney World Swan, May 28th–30th, 2009, Orlando, Florida, USA

American Medical Informatics Association (AMIA) Spring Congress, Walt Disney World Swan, May 28th–30th, 2009, Orlando, Florida, USA


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    • 1. EVOLVE: INTEGRATED GLOBAL EARLY WARNING AND RESPONSE SYSTEM Innovative Support to Emergencies, Diseases, and Disasters Photo credit: IRMA (Integrated Risk Management for Africa) AMIA Spring Congress Walt Disney World Swan May 28 th –30 th , 2009, Orlando, Florida, USA Taha Kass-Hout, MD, MS Director, Global Public Health and Informatics
    • 2. Overview
      • Infectious disease events represent substantial morbidity , mortality , and socio-economic impact
    • 3. Late Detection – Response DAY CASES Opportunity for control
    • 4. Early Detection and Response DAY CASES Opportunity for control
    • 5. Public Health Measures 1000 Malaria infections (100%) 50 Malaria notifications (5%) Specificity / Reliability Sensitivity / Timeliness
        • Main attributes
          • Representativeness
          • Completeness
          • Predictive value positive
      Get as close to the bottom of the pyramid as possible Urge frequent reporting: Weekly  daily  immediately
    • 6. Public Health Measures Time
        • Main attributes
          • Timeliness
      Health care hotline Signal as early as possible
    • 7.
      • One of four major initiatives of the UN Millennium Action Plan (2000)
        • mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World (2009)
      Making Mobile Tech Work for Health Photo Source: UN Foundation
    • 8. Making Mobile Tech Work for Health Avian Influenza Exercise: Stung Treng Province, Cambodia, October 13-15, 2008 SE Asia Region (Source: Wikipedia) The Komphun rural Health Center serves over 7000 population in the Stung Treng and neighboring provinces. Cell phone use during the Avian Influenza Exercise: Stung Treng Province, Cambodia, October 13-15, 2008
    • 9. Growth of Mobile Technologies Adapted from Dzenowagi, WHO, 2005
    • 10.
        • Internet penetration levels among the population as a whole
          • India 5.2%
          • Malaysia 59.0%
          • Thailand 20.5%
          • Myanmar 0.1%
        • This compares to about 73.6% for North America
          • Some countries in Asia are also shown to be high such as Japan, S. Korea, Taiwan and Hong Kong
      Internet Penetration in Asia Pacific Nigel Collier, BioCaster: http://biocaster.nii.ac.jp Data Source: http://www.internetworldstats.com/stats3.htm#asia
    • 11. Urban – Rural Population, SE Asia UNCTAD Handbook of Statistics 2004 Adapted from Dzenowagi, WHO, 2005 Year: 2002
    • 12. Our Approach
      • Hybrid human and machine-based
      • Collaborative and cross-disciplinary
      • Web 2.0/3.0 (geo-semantic) platform
    • 13. Information Sources
      • 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, …
    • 14. Evolve Architecture and Processes Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual ISDS Conference, 2008 http://kasshout.blogspot.com/2008/12/best-poster-award-for-improving-public.html
    • 15. Evolve Architecture and Processes 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 generation esting
    • 16. Evolve Related items (e.g., News articles) are grouped into a thread. Threads are later associated with events (hypothesized or confirmed). Collaborative-centric semantic tags Expert-generated semantic tags Publish and Share Information Create a filter (by keyword, tag, topic, location, or time) and subscription (email, GeoRSS, SMS Text Messaging, Twitter, etc.) An event is monitored through a thread of items Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS List view Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    • 17. Evolve Expert-centric auto-generated (machine-learning) semantic tags and related items Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS Tags are semantically ranked (a statistical possibility match). Users can further train the classifier by rejecting a suggestion. Users can also train the geo-locator by rejecting or updating a location . Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    • 18. Evolve Map view Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS Semantic map to monitor topic rise or decay over time Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    • 19. Evolve Filter feature which automatically filters content by topic of interest Filter content by radius Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    • 20. Automatic Classification
      • Current classification includes:
        • 7 syndromes
        • 10 transmission modes
        • > 100 infectious diseases
        • > 180 micro-organisms
        • > 140 symptoms
        • > 50 chemicals
    • 21. Indicators and Insights
      • Approximations of Epidemiological Features
      • Response
      • Local Public Community Reaction (Public and Responders)
      • Infrastructure
      • Infectious Disease Disaster
    • 22. Snapshot: SE Asia, 2008-2009
      • From September 1, 2008 to February 27, 2009
      • 998 near real-time reports on
        • 46 infectious diseases that effect humans or animals
        • Myanmar, Thailand, Laos, Cambodia, and Vietnam
        • 220 provinces, 239 districts, and 14 cities
      Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS
    • 23. Snapshot: SE Asia, 2008-2009
      • From September 1, 2008 to February 27, 2009
      • The infectious disease event reporting in SE Asia was of:
        • Low socioeconomic disruption (83%),
        • High socioeconomic disruption (17%); with indicators of:
          • potential sociological crisis (16.4%), and
          • disaster (0.6%)
      Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS
    • 24. Influenza A(H1N1), 2009 Data source: Google Insights for Search http://www.google.com/insights/search/#q=%22swine%20flu%22%2C%22avian%20flu%22&cmpt=q avian flu swine flu
    • 25. Influenza A(H1N1), 2009 Data source: HashTags.org monitoring Twitter http://hashtags.org/tag/swineflu/messages
    • 26. Influenza A(H1N1), 2009 Data source: A(H1N1) Evolve Collaborative Workspace http://riff.instedd.org/space/SwineFlu Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    • 27. Influenza A(H1N1), 2009 Data source: A(H1N1) Evolve Collaborative Workspace http://riff.instedd.org/space/SwineFlu Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD Mid-March 2009 thru May 19 th 2009
    • 28. Influenza A(H1N1), 2009 Data source: A(H1N1) Evolve Collaborative Workspace http://riff.instedd.org/space/SwineFlu Mid-March 2009 thru May 19 th 2009 Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    • 29. Influenza A(H1N1), 2009 Data source: A(H1N1) Evolve Collaborative Workspace http://riff.instedd.org/space/SwineFlu Mid-March 2009 thru May 19 th 2009 Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    • 30. Avian Influenza: Egypt, 2009 Tracking the recent Avian Influenza Outbreak in Egypt (reports started to appear late January 2009). Data source: Africa Evolve Collaborative Workspace http://riff.instedd.org/space/AfricaAlerts
    • 31. Worldwide Health Events, 2008 Data source: Early Detection and Response Evolve Collaborative Workspace http://riff.instedd.org/space/DEMOEventDetection
    • 32. Acknowledgment
    • 33. Through Funding from…
    • 34. Thank You! In STEDD 400 Hamilton Avenue, Suite 120 Palo Alto, CA 94301, USA +1.650.353.4440 +1.877.650.4440 (toll-free in the US) [email_address] Cambodia, Photo taken by Taha Kass-Hout, October 2008 “ this pic says it all- our kids are all the same- they deserve the same ”, Comment by Robert Gregg on Facebook, October 2008