{"id":10307,"date":"2010-09-23T09:17:19","date_gmt":"2010-09-23T09:17:19","guid":{"rendered":"http:\/\/planetsave.com\/?p=10307"},"modified":"2010-09-23T09:17:19","modified_gmt":"2010-09-23T09:17:19","slug":"social-network-data-predicts-disease-outbreaks-video","status":"publish","type":"post","link":"https:\/\/planetsave.com\/articles\/social-network-data-predicts-disease-outbreaks-video\/","title":{"rendered":"Social Network Data Predicts Disease Outbreaks [VIDEO]"},"content":{"rendered":"

\"\"<\/a>If you are an epidemiologist, that is, a scientist who studies diseases and their spread throughout a population, early detection of health trends is crucial to staying ahead of an outbreak and potentially saving many lives.<\/h3>\n

In a new TED talk (and video), Harvard Professor of Medicine Nicholas Christakis, reveals how data from social network sites can be used to predict a disease outbreak–before it grows into a major health crisis.<\/p>\n

Professors Christakis and Fowler explore the nature of social Networks (SN) and reveal the rules that govern how SNs form. Knowing these rules allows us to predict, and possibly prevent, new disease epidemics.<\/h3>\n

Professor Christakis and his colleague James Fowler spent a lot of time mapping the intricate interconnections that comprise our various social networks. Their work shows that the dynamics of SN (such as which persons in a network constitute its major nodes or “hubs”, members that are the most connected to<\/em>) can be used to predict the spread of a disease within an interconnected population.<\/p>\n

On a lighter note, the same techniques can be used to predict how “good” an idea (or even a new product) is by tracking how it spreads through a network. Of course, whether and how an idea or virus spreads through a network depends on the nature of the relationships between members of the network; members can be friends, family, co-workers, colleagues, teammates, neighbors and\/or or sexual partners. Not all connections in a network are equal.<\/p>\n

To learn more about this network mapping, watch the video below of Nicholas Christakis speaking on how social networks predict epidemics (article continues after the video).<\/strong><\/p>\n