Integrated Global Early Warning And Response
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Innovative Support to Emergencies, Diseases, and Disasters INTEGRATED GLOBAL EARLY WARNING AND RESPONSE 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
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Combining the power of the Web with the ubiquity and accuracy of mobile computing, InSTEDD helps teams and organizations communicate, share and analyze information more seamlessly, make better decisions, and take more effective action in the face of a public health threat or natural disaster. The In-STEDD Collaboration Suite consists of three open-source software tools that help public health users detect anomalies, visualize clusters of potential events, predict the rate and spread of a disease out-break and provide decision makers with tools, me-thodologies and processes to investigate the event. These software tools are: • InSTEDD Mesh4X is a light-weight synchroniza-tion platform based on cloud computing and peer-to-peer architectures. This integration platform al-lows teams to share critical information reliably, selectively, timely and securely, with anyone, using any device, regardless of network connection, sys-tems or services. Network connectivity is not a constant requirement, as Mesh4x can collect and distribute updates among users over SMS, Internet, or through other available means. • InSTEDD Evolve is a set of integrated services (analytic, collaborative and visualization) that helps to build and sustain a community of domain experts from the local to the international level. Evolve is equipped with a machine learning algo-rithm that augment users input and accurately classifies 7 syndromes, 10 transmission modes, over 100 infectious diseases, over 180 micro-organisms, over 140 symptoms, and over 50 chemicals. • InSTEDD GeoChat is a communications system that lets teams coordinate around events as they unfold, linking field, headquarters, and the local community in a unified effective response.Presently, these tools are being piloted in the Mekong Basin region of Southeast Asia (Cambodia, Thailand, Laos, and Cambodia), Ushahidi in Kenya, HIV clinics in rural Tanzania, Mongolia, Ghana, and Bangladesh.Use of mobile telephones for public health events detection and intervention in developing countries has tremendous potential due to low start-up cost and low bandwidth (e.g., SMS gateway). In 2002, mobile subscribers overtook fixed line subscribers worldwide and this occurred across geographic re-gions, socio-demographic criteria (gender, income, age) or economic criteria such as GDP per capita1. In much of sub-Saharan Africa, there are more mobile phones than fixed-line phones2 and the use of mobile phones in many Asian countries is on the rise. Addi-tionally, mobile telephone subscriptions have been growing rapidly since the 1980s in both developing and developed countries. Furthermore, the demand for mobile phones exists beyond reducing the waiting list for traditional wire-line phones 3.However, technical, financial and regulatory barriers remain great challenges to fully adopting mobile technology as a platform in developing countries. Using mobile phones for health data exchange in developing countries has not been demonstrated giv-en the costs of data transmitted over mobile phone are greater than voice costs 2, 4, language and illitera-cy barriers2, and privacy and security issues since SMS messages can leave a trail. Other challenges include the physical components of a telephone (headset or network) are not isolated but are part of an entire business model that includes pricing plans and other incentives which can provide leverage em-ployed by public health agencies and policymakers.References1. Feldmann V: Mobile overtakes Internet: Implica-tions for Policy and Regulation. International Tele-commjunications Union 2003:1-39 [http://www.itu.int/osg/spu/ni/mobileovertakes/Resources/Mobileovertakes_Paper.pdf]. last accessed 13 March 2009.2. Vodafone Policy Paper: Africa: The Impact of Mobile Phones. Vodafone Policy Paper Series Number 2 2005 [http://www.vodafone.com/assets/files/en/AIMP_09032005.pdf]. last accessed 13 March 2009. 3. Kundu A, Sarangi N: ICT and Human Develop-ment: Towards Bui
Transcript of Integrated Global Early Warning And Response
Innovative Support to Emergencies, Diseases, and Disasters
INTEGRATED GLOBAL EARLY WARNING AND RESPONSE
Photo credit: IRMA (Integrated Risk Management for Africa)
AMIA Fall, 2009Experiences and Challenges in Global Health Informatics PanelNov 15th, 2009, San Francisco, CA, USA
Taha Kass-Hout, MD, MS
The Team
Eduardo (Ed) Jezierski Nicolas di Tada Dennis Israelski, MD Eric D. Rasmussen, MD, MDM, FACP
Overview
Infectious disease events represent substantial morbidity, mortality, and socio-economic impact
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
Photo Source: Nellie Bristol, Are Cell Phones Leading the mHealth Revolution, the Global Health Magazine, 2009.
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
SE Asia Region (Source: Wikipedia)
The Komphun rural Health Center serves over 7000 population in the Stung Treng and neighboring provinces.
Avian Influenza: 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
Our Approach
Hybrid human and machine-based
Collaborative and cross-disciplinary
Web 2.0, Light-weight and open source
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, …
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
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
Collaborative Surveillance
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
Collaborative Surveillance
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
Collaborative SurveillanceMap view
Data source: SE Asia 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
Collaborative Surveillance
Filter content by
radius
Data source: SE Asia 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
2009 Novel Influenza A(H1N1)
Data source: 2009 Novel Influenza A(H1N1) Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
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 19th 2009
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
Data source: 2009 Novel Influenza A(H1N1) Collaborative Workspace http://riff.instedd.org/space/SwineFlu
Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD
2009 Novel Influenza A(H1N1)
Mid-March 2009 thru May 19th 2009
Data source: 2009 Novel Influenza A(H1N1) Collaborative Workspace http://riff.instedd.org/space/SwineFlu
2009 Novel Influenza A(H1N1)
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).
Data source: Africa Collaborative Workspacehttp://riff.instedd.org/space/AfricaAlerts
Worldwide Health Events, 2008
Data source: Early Detection and Response 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
