Integrated Global Early Warning and Response System

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  • First, I am pleased that so many trials of cell phones are going forward despite claims by some that providing cell-phone access might be unethical Second, the report fails to distinguish between anecdotal evidence and rigorous evaluation results Third, this report does not address the issue of how to design incentives for truthful data collection by cell phone
  • In 2002, mobile subscribers overtook fixed line subscribers worldwide and this occurred across geographic regions, socio-demographic criteria (gender, income, age) or economic criteria such as GDP per capita 1 1. Feldmann V: Mobile overtakes Internet: Implications for Policy and Regulation. International Telecommjunications Union 2003:1-39 [http://www.itu.int/osg/spu/ni/mobileovertakes/Resources/Mobileovertakes_Paper.pdf]. last accessed 13 March 2009.
  • The center’s director already uses SMS to communicate potential outbreak information or suspect AI cases to PHD and CDC
  • There is currently NO turnkey solution to this problem… You have to involve humans and provide a collaborative environment for these people to work together… and we’re adopting a web 2.0/3.0 approach to pull everything together: In the Pepto Bismol example, the most interesting aspects of this event was that the majority of the victims did not seek medical attention at first. The Milwaukee Health Department in 1993 became aware of widespread gastrointestinal illness in the community through phone calls from citizens and the media. There was increased absenteeism from schools and businesses, and groceries and pharmacies reported depletion of anti-diarrheal medications. In an event like this, a human expert could associate certain indications and arrive at a conclusion or a few hypotheses to corroborate or refute an event: There have been unusually heavy rains for the last few weeks The Water authority has received several complaints about cloudy water from customers Now we have all these calls and concerns from the community So perhaps I should lean towards a waterborne hypothesis vs. something else… the human eye can also quickly detect a cluster of pins on a map over time and space and make certain assumptions… As we’re faced with a cross-disciplinary problem (human, animal, environment, organisms, etc.) it becomes more clear that we need to offer a collaborative space for experts from multiple fields to work together on solving the problem Back when I was in the trenches of SARS, we found out very quickly the importance of crowdsourcing and the need to share certain types of data quickly
  • It is not necessarily lack of information… we have a lot of information… rather, can we put the information into intelligence (or context) in a timely manner? Multiple streams include the following- say something about why you need to stitch multiple sources together... Sidebar: 5/50 rule, in 5 years time, 50% of all content will be user-generated: (Reference: The Podshow by Ron Bloom (http://www.ronbloom.com/?p=11) 60% content has geo-spatial and temporal aspects… Image Sources: Wikipedia: http://www.citris-uc.org/system/files/imce-u10/Wikipedia-logo.png Blogger: http://z.about.com/d/weblogs/1/5/V/-/-/-/BloggerHomePage.PNG OpenMRS: http://ruddzw.files.wordpress.com/2007/05/openmrs_osx.png Remote Sensing: http://www.medscape.com/content/2000/00/41/47/414717/art-e0603.01.fig2.jpg Cell phone/iPhone; http://healthinformaticsblog.files.wordpress.com/2008/03/iphone-denticon-patient-thumb.jpg WhoIsSick.org: http://gmapsmania.googlepages.com/whosickgmm.JPG
  • Essentially, we’re connecting the dots… by “socializing” the content and reasoning the information – that is the human input and interpretation backed up with machine learning algorithms in an environment that fosters hypotheses generation, testing and findings. This is especially true of newly emerging events, users can collaborate and test different hypotheses against a body of evidence as the event unfolds over time and space… The way we are classifying events is not black or white… we are also modeling the great grey in between…We’re currently looking at classifying different syndromes, symptoms, transmission modes, organisms and micro-organisms, chemicals, and diseases… Additionally, we’re looking into supporting multiple ontologies (like the SNOMED, ICDs, LOINC, etc.) across different languages and disciplines…
  • To recap, The human experts interacting with automated systems The collaborative decision making environment I am sure one day soon we will have an EID impact assessment... just like there is an environmental impact assessment… Thank you VERY much for your time today…
  • To recap, The human experts interacting with automated systems The collaborative decision making environment I am sure one day soon we will have an EID impact assessment... just like there is an environmental impact assessment… Thank you VERY much for your time today…
  • Integrated Global Early Warning and Response System

    1. 1. INTEGRATED GLOBAL EARLY WARNING AND RESPONSE Innovative Support to Emergencies, Diseases, and Disasters Photo credit: IRMA (Integrated Risk Management for Africa) AMIA Fall, 2009 Experiences and Challenges in Global Health Informatics Panel Nov 15 th , 2009, San Francisco, CA, USA Taha Kass-Hout, MD, MS
    2. 2. The Team <ul><li>Eduardo (Ed) Jezierski </li></ul><ul><li>Nicolas di Tada </li></ul><ul><li>Dennis Israelski, MD </li></ul><ul><li>Eric D. Rasmussen, MD, MDM, FACP </li></ul>
    3. 3. Overview <ul><li>Infectious disease events represent substantial morbidity , mortality , and socio-economic impact </li></ul>
    4. 4. <ul><li>One of four major initiatives of the UN Millennium Action Plan (2000) </li></ul><ul><ul><li>mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World (2009) </li></ul></ul>Making Mobile Tech Work for Health Photo Source: UN Foundation Photo Source: Nellie Bristol, Are Cell Phones Leading the mHealth Revolution, the Global Health Magazine, 2009.
    5. 5. Growth of Mobile Technologies Adapted from Dzenowagi, WHO, 2005
    6. 6. <ul><ul><li>Internet penetration levels among the population as a whole </li></ul></ul><ul><ul><ul><li>India 5.2% </li></ul></ul></ul><ul><ul><ul><li>Malaysia 59.0% </li></ul></ul></ul><ul><ul><ul><li>Thailand 20.5% </li></ul></ul></ul><ul><ul><ul><li>Myanmar 0.1% </li></ul></ul></ul><ul><ul><li>This compares to about 73.6% for North America </li></ul></ul><ul><ul><ul><li>Some countries in Asia are also shown to be high such as Japan, S. Korea, Taiwan and Hong Kong </li></ul></ul></ul>Internet Penetration in Asia Pacific Nigel Collier, BioCaster: http://biocaster.nii.ac.jp Data Source: http://www.internetworldstats.com/stats3.htm#asia
    7. 7. Urban – Rural Population, SE Asia UNCTAD Handbook of Statistics 2004 Adapted from Dzenowagi, WHO, 2005 Year: 2002
    8. 8. Making Mobile Tech Work for Health Avian Influenza: 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. 9. Our Approach <ul><li>Hybrid human and machine-based </li></ul><ul><li>Collaborative and cross-disciplinary </li></ul><ul><li>Web 2.0, Light-weight and open source </li></ul>
    10. 10. Information Sources <ul><li>Event-based ad-hoc unstructured reports issued by formal or informal sources </li></ul><ul><li>Indicator-based (number of cases, rates, proportion of strains…) </li></ul>Timeliness, Representativeness, Completeness, Predictive Value, Quality, …
    11. 11. 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
    12. 12. 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
    13. 13. Collaborative Surveillance 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
    14. 14. Collaborative Surveillance 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
    15. 15. Collaborative Surveillance Map view Data source: SE Asia 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
    16. 16. Collaborative Surveillance Filter feature which automatically filters content by topic of interest Filter content by radius Data source: SE Asia Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    17. 17. Automatic Classification <ul><li>Current classification includes: </li></ul><ul><ul><li>7 syndromes </li></ul></ul><ul><ul><li>10 transmission modes </li></ul></ul><ul><ul><li>> 100 infectious diseases </li></ul></ul><ul><ul><li>> 180 micro-organisms </li></ul></ul><ul><ul><li>> 140 symptoms </li></ul></ul><ul><ul><li>> 50 chemicals </li></ul></ul>
    18. 18. Indicators and Insights <ul><li>Approximations of Epidemiological Features </li></ul><ul><li>Response </li></ul><ul><li>Local Public Community Reaction (Public and Responders) </li></ul><ul><li>Infrastructure </li></ul><ul><li>Infectious Disease Disaster </li></ul>
    19. 19. Snapshot: SE Asia, 2008-2009 <ul><li>From September 1, 2008 to February 27, 2009 </li></ul><ul><li>998 near real-time reports on </li></ul><ul><ul><li>46 infectious diseases that effect humans or animals </li></ul></ul><ul><ul><li>Myanmar, Thailand, Laos, Cambodia, and Vietnam </li></ul></ul><ul><ul><li>220 provinces, 239 districts, and 14 cities </li></ul></ul>Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS
    20. 20. Snapshot: SE Asia, 2008-2009 <ul><li>From September 1, 2008 to February 27, 2009 </li></ul><ul><li>The infectious disease event reporting in SE Asia was of: </li></ul><ul><ul><li>Low socioeconomic disruption (83%), </li></ul></ul><ul><ul><li>High socioeconomic disruption (17%); with indicators of: </li></ul></ul><ul><ul><ul><li>potential sociological crisis (16.4%), and </li></ul></ul></ul><ul><ul><ul><li>disaster (0.6%) </li></ul></ul></ul>Data source: SE Asia Evolve Collaborative Workspace http://riff.instedd.org/space/ProMed-MBDS
    21. 21. 2009 Novel Influenza A(H1N1) Data source: 2009 Novel Influenza A(H1N1) Collaborative Workspace http://riff.instedd.org/space/SwineFlu Yin Myo Aye, MD: ProMED MBDS Taha Kass-Hout, MD, MS: InSTEDD
    22. 22. 2009 Novel Influenza A(H1N1) Data source: 2009 Novel Influenza A(H1N1) 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
    23. 23. 2009 Novel Influenza A(H1N1) Data source: 2009 Novel Influenza A(H1N1) 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
    24. 24. 2009 Novel Influenza A(H1N1) Data source: 2009 Novel Influenza A(H1N1) 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
    25. 25. Avian Influenza: Egypt, 2009 Tracking the recent Avian Influenza Outbreak in Egypt (reports started to appear late January 2009). Data source: Africa Collaborative Workspace http://riff.instedd.org/space/AfricaAlerts
    26. 26. Worldwide Health Events, 2008 Data source: Early Detection and Response Collaborative Workspace http://riff.instedd.org/space/DEMOEventDetection
    27. 27. Acknowledgment
    28. 28. Through Funding from…
    29. 29. 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

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