Ensemble Modeling Can Track Flu Outbreaks in Real Time


Study used Twitter posts, Google searches, electronic medical records and crowdsourced data

Influenza outbreaks can be accurately tracked in real time using data analytics, according to a recent study published in the PLOS Computational Biology Journal.

Conducted by researchers at Boston’s Children’s Hospital, the study used ensemble modeling, which analyzes data from multiple sources of information. The study used data from four different sources to generate real-time insights on flu outbreaks affecting specific populations:

  • Google searches from Jul 2013-Feb 2015
  • Electronic health record data (athenahealth)
  • Crowd-sourced data from Healthmaps Flu Near You Website
  • Twitter messages from Nov 2011-Feb 2015

In reporting the study, Boston Children’s Hospital Chief Innovation Officer John Brownstein said in a Nov. 3 blog post:  “What have people in informatics, medicine and public health dreamed of for years? The ability to leverage all manner of data — historic, social, EHR and so on — to create a learning health system.”

The study’s results correlated 90% with findings from the Center for Disease Control and Prevention and were much more accurate than tracking using only a single source of data and operated in real time, according to Boston Children’s Hospital. Researchers hope to expand their analytics model to track other diseases and also target narrower geographic areas.

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Sue Walsh

About Sue Walsh

Sue Walsh is News Writer for RTInsights, and a freelance writer and social media manager living in New York City. Her specialties include tech, security and e-commerce. You can follow her on Twitter at @girlfridaygeek.

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