Evolve: InSTEDD's Global Early Warning and Response System
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Transcript of Evolve: InSTEDD's Global Early Warning and Response System
Innovative Support to Emergencies, Diseases, and Disasters
EVOLVE: INTEGRATED GLOBAL EARLY WARNING AND RESPONSE SYSTEM
Photo credit: IRMA (Integrated Risk Management for Africa)
AMIA Spring Congress Walt Disney World SwanMay 28th–30th, 2009, Orlando, Florida, USA Taha Kass-Hout,
MD, MSDirector, Global Public Health and Informatics
Overview
Infectious disease events represent substantial morbidity, mortality, and socio-economic impact
DAY
CA
SE
S
Opportunity for control
Late Detection – Response
DAY
CA
SE
S
Opportunity for control
Early Detection and Response
1000 Malaria infections (100%)
50 Malaria notifications (5%)
Get as close to the bottom of the pyramid
as possible
Urge frequent reporting: Weekly daily immediately
Specificity / Reliability
Sensitivity / Timeliness
• Main attributeso Representativenesso Completenesso Predictive value positive
Public Health Measures
Signal as early
as possible
Time
• Main attributeso Timeliness
Health care hotline
Public Health Measures
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
SE Asia Region (Source: Wikipedia)
The Komphun rural Health Center serves over 7000 population in the Stung Treng and neighboring provinces.
Avian Influenza Exercise: Stung Treng Province, Cambodia, October 13-15, 2008
Cell phone use during the Avian Influenza Exercise: Stung Treng Province, Cambodia, October 13-15, 2008
Making Mobile Tech Work for Health
Growth of Mobile Technologies
Adapted from Dzenowagi, WHO, 2005
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
Nigel Collier, BioCaster: http://biocaster.nii.ac.jp Data Source: http://www.internetworldstats.com/stats3.htm#asia
Internet Penetration in Asia Pacific
UNCTAD Handbook of Statistics 2004
Urban – Rural Population, SE Asia
Adapted from Dzenowagi, WHO, 2005
Year: 2002
Our Approach
Hybrid human and machine-based
Collaborative and cross-disciplinary
Web 2.0/3.0 (geo-semantic) platform
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, …
Evolve Architecture and Processes
Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual ISDS Conference, 2008http://kasshout.blogspot.com/2008/12/best-poster-award-for-improving-public.html
Feature extraction, reference and baseline information
Tags
Multiple Data Streams
User-Generated and Machine Learning Metadata
Comments
Spatio-temporal
Flags/Alerts/Bookmarks
Evo
lve Bo
tEvent Classification,
Characterization and Detection
Previous Event Training Data
Previous Event Control Data
Metadataextraction
Machine learning
Social network
Professional feedback
Anomaly detection
Collaborative Spaces
Hypotheses generation\testing
Evolve Architecture and Processes
Related items (e.g., News articles) are grouped into a thread. Threads are
later associated with events (hypothesized or confirmed).
Collaborative-centric
semantic tags
Evolve
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 Workspacehttp://riff.instedd.org/space/ProMed-MBDS
List view
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Expert-centric auto-generated
(machine-learning)
semantic tags and related
items
Evolve
Data source: SE Asia Evolve Collaborative Workspacehttp://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 MBDSTaha Kass-Hout, MD, MS: InSTEDD
EvolveMap view
Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS
Semantic map to monitor topic rise or decay
over time
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Filter feature which automatically filters content
by topic of interest
Evolve
Filter content by
radius
Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Automatic Classification
Current classification includes: 7 syndromes 10 transmission modes > 100 infectious diseases > 180 micro-organisms > 140 symptoms > 50 chemicals
Indicators and Insights
Approximations of Epidemiological Features
Response Local Public Community Reaction (Public
and Responders) Infrastructure Infectious Disease Disaster
Snapshot: SE Asia, 2008-2009From 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 Workspacehttp://riff.instedd.org/space/ProMed-MBDS
Snapshot: SE Asia, 2008-2009From 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 Workspacehttp://riff.instedd.org/space/ProMed-MBDS
Data source: Google Insights for Searchhttp://www.google.com/insights/search/#q=%22swine%20flu%22%2C%22avian%20flu%22&cmpt=q
Influenza A(H1N1), 2009
avian flu
avian flu
swine flu
swine flu
Data source: HashTags.org monitoring Twitterhttp://hashtags.org/tag/swineflu/messages
Influenza A(H1N1), 2009
Influenza A(H1N1), 2009
Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Influenza A(H1N1), 2009
Mid-March 2009 thru May 19th 2009
Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu
Influenza A(H1N1), 2009
Mid-March 2009 thru May 19th 2009
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Influenza A(H1N1), 2009
Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu
Mid-March 2009 thru May 19th 2009
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Avian Influenza: Egypt, 2009
Tracking the recent Avian Influenza
Outbreak in Egypt (reports started to
appear late January 2009).
Tracking the recent Avian Influenza
Outbreak in Egypt (reports started to
appear late January 2009).
Data source: Africa Evolve Collaborative Workspacehttp://riff.instedd.org/space/AfricaAlerts
Worldwide Health Events, 2008
Data source: Early Detection and Response Evolve Collaborative Workspacehttp://riff.instedd.org/space/DEMOEventDetection
Acknowledgment
Through Funding from…
InSTEDD400 Hamilton Avenue, Suite 120
Palo Alto, CA 94301, USA
+1.650.353.4440
+1.877.650.4440 (toll-free in the US)
Thank You!
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