Last week, InSTEDD stood up a workspace to further aid experts and responders collaborating around emerging reports related to the 2009 H1N1 pandemic influenza event.
The workspace is based on InSTEDD’s Riff, an online application which allows a team to collaborate around multiple streams of information to assess, characterize, and respond to an event with the assistance of automated services.
Our Riff Space offers a comprehensive set of collaborative features, including:
- commenting,
- tagging,
- mapping (both user generated and automated),
- the relating of multiple alerts to each other,
- searching and filtering (by keyword and by location),
- specifying a time window,
- adding attachments,
- subscribing (currently in the form of a web-friendly format known as GeoRSS and through an email subscription),
and more.
The Riff Workspace is also equipped with an intelligent process (sometimes referred to as a machine-learning algorithm) that “learns” from anything provided by human experts (e.g. adding a keyword or a tag, or correcting the incorrect mapping of an item). This intelligent process quickly and accurately learns to follow advice from expert humans and we’re showing a 95% confidence level for the automated selections based on previous tests. The system soon starts suggesting tags, as well as correcting itself, and gradually offers even better results over time.
Have you an H1N1 alert or item you like to share with the community? You can easily contribute that alert by clicking the “Add Item” feature on our Riff H1N1 workspace.
If you have a background in public health, international relations, diplomacy, social work, or emergency response and are interested in contributing actively to this effort please contact us at info@instedd.org.
For low volume announcements, you can follow us on Twitter. As I also mentioned in an earlier blog, we’re following very rapid news events related to H1N1 epidemiology through Veratect on Twitter here.
Related Links:
- Collaborative Analytics and Environment for Linking Early Event Detection to an Effective Response
- Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual International Society for Disease Surveillance Conference