InSTEDD Riff at PHIN 09: An Integrated Global Early Warning and Response System

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We present on the anticipated contribution of social networking, machine learning and collaboration techniques to address public health events (e.g., an emerging infectious disease outbreak or a …

We present on the anticipated contribution of social networking, machine learning and collaboration techniques to address public health events (e.g., an emerging infectious disease outbreak or a pandemic). We describe a hybrid (event-based and indicator-based) surveillance system (InSTEDD Riff) designed to streamline the collaboration between domain experts and machine learning algorithms for linking detection to an effective response. Presently, Riff and its associated modules are being piloted in the Mekong Basin region of Southeast Asia.

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  • When trying to do information monitoring on any specific subject or public health surveillance in this case, we face a well known problem these days: Information overload and a lack of homogeneity in the sources of that information. Different standards are used, reports include different level of detail for the information and we have both event-based information and indicator-based reports.
  • Our approach with Riff is based on creating a platform that supports the collaboration of a cross-disciplinary team with machine-learning tools to aid them in finding patterns, trends or clusters in the information and fostering the exploration of hypothesis and feedback collection from the field. This way we get the best of the two worlds: Applied expertise and human intelligence in finding patterns, getting insights and even following hunches The processing power of machine-learning tools enabling the scanning and classification of huge amount of data 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. SHOW FLASH ANIMATION
  • 1- Information get’s collected from different sources 2-Information get’s decorated with different layers of data, like remote sensing information about temperature, humidity or terrain. 3-Machine learning modules classify the articles in the system, determining location, name of diseases, symptoms or syndromes, extracting structured data like epidemiological numbers of suspected or confirm cases. 4-Experts from different disciplines collaborate around the information, creating comments, tagging, relating articles and correcting or training machine-learning algorithms. 5-Experts can use different visualizations and filtering tools, to explore the body of evidence as the event unfolds over time and space and create hypothesis of events that they can discuss or refine with their team members and decide whether they think that a field investigation is needed. 6-Field staff can collect and report information that gets incorporated back to the system.
  • Saved filters with subscriptions List, Grid or Map views -Tags -Related items Publish and share information through RSS feeds
  • We have maps with locations and heatmaps.
  • And of course, you can combine filters by tags, with filters by region or any other property that the article has in the system.
  • As a space gets
  • 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 (Emerging Infectious Disease) impact assessment... just like there is an environmental impact assessment… Thank you VERY much for your time today…

Transcript

  • 1. Innovative Support to Emergencies, Diseases, and Disasters Photo credit: IRMA (Integrated Risk Management for Africa) PHIN Conference 2009 Hyatt Regency, Downtown Atlanta Aug.30-Sept.3, Atlanta, Georgia, USA Nicolás di Tada Director of Platform RIFF (a.k.a. EVOLVE)
  • 2.
    • Event-based ad-hoc unstructured reports issued by formal or informal sources
    • Indicator-based (number of cases, rates, proportion of strains…)
  • 3.
    • Health surveillance is a multi-field problem
      • Humans
      • Animal
      • Environment
      • Organisms
      • Chemichals
  • 4.  
  • 5.
    • Hybrid human and machine-learning
    • Collaborative and cross-disciplinary
    • Free, Open Source, modular
    • http://riff.instedd.org
  • 6. 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
  • 7. Saved filters with subscriptions Extracted articles (full text) Stars, comments, flags, location Tags (automatic classification + humans) Related Items Tag Cloud
  • 8. Locations Heatmap
  • 9.  
  • 10.  
  • 11. 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
  • 12. 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
  • 13.
    • Current classifications includes:
      • 7 syndromes
      • 10 transmission modes
      • > 100 infectious diseases
      • > 180 micro-organisms
      • > 140 symptoms
      • > 50 chemicals
  • 14.
    • 480 Registered users
    • 394 collaboration spaces
    • 694 registered input streams of information
    • 900.000 articles analyzed
    • 443.151 geocoded positions
    • 700 terms ‘trained’ on the geocoder by human users
    • 804 comments on items left by people
    • 12.000+ tags added by human users
  • 15.
    • In Progress
    • Ontologies (SNOMED, BioCaster, ICD)
    • Event reporting, analysis and public announcements (via Reuters)
    • Planned
    • API for external extensions and interactions
    • Full support of structured data
    • Automatic field data collection through forms
    • Anomaly detections
  • 16.  
  • 17.  
  • 18. 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] org Nicolas di Tada [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 http://riff.instedd.org