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Social Networks Predict Disease Spread

Scientists at Harvard University in Cambridge, Massachusetts and The University of California at San Diego determined a method of predicting the spread of contagious diseases using social networking theories. Utilizing the friendship paradox – the idea that a person’s friends are likely more popular than they are – the researchers were able to follow groups closer to the center of a network without the tedious and difficult chore of fully mapping social networks. Members closer to the center of a network have contact with more people and are more likely to contract any contagious disease than people toward the outskirts of the group.
Dr. James Fowler at UCSD tracked 319 randomly selected Harvard students through the 2009 flu season. (Source: University of California at San Diego)
Dr. James Fowler at UCSD tracked 319 randomly selected Harvard students through the 2009 flu season. (Source: University of California at San Diego)

Dr. Nicholas Christakis of the Harvard Medical School and his collaborator Dr. James Fowler at UCSD tracked 319 randomly selected Harvard students through the 2009 flu season as well as 425 students named as friends by the original group using two different methods of identifying the flu. The flu manifested itself in the friends group two weeks sooner than in the random group using one identification method and 46 days sooner using the other method.

The sooner outbreaks of infectious disease are identified, the sooner they can be treated and, hopefully, contained. Even a two week head start as seen in the more conservative identification method could make an enormous difference in the size and severity of a major outbreak. That said, it’s likely the amount of lead time inherent in this method will likely vary from case to case depending on the specifics of the infection and the details of the network. It also depends on actively monitoring a network likely to be affected by disease before it strikes.

In addition to tracking infection diseases, the friends paradox could also be used to track other things that spread out among groups of people including trends, the adoption of specific behaviors, and the effectiveness of marketing campaigns.

TFOT previously reported on another social networking research project involving the voluntary publication of data collected via RFID chips, the decision by the individuals of who individuals should see the information and what data they decide to delete or retain for public consumption. TFOT also reported on the origins and spread of infectious diseases including a historical overview of smallpox and a video about the H1N1 flu epidemic.

Read more about the use of social networking to predict disease spread in this paper published at PLOS One (PDF).

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About

Janice Karin has a B.A in physics from the University of Chicago and a M.S. in physics from the University of Pennsylvania. In addition to extensive experience as a technical writer focused on development tools, databases, and APIs, Janice has worked as a freelance reporter, editor, and reviewer with contributions to a variety of technology websites. One of her primary focuses has been on PDAs and mobile devices, but she is interested in many other areas of science and technology.

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