Catching the flu is never fun, but your circle of friends or social networks may help predict just when and where you will contract the virus. This method has been dubbed the “friendship paradox” and was formulated by Nicholas Christakis, professor of medicine, medical sociology and sociology at Harvard University, and James Fowler, professor of medical genetics and political science at the University of California, San Diego. So what is this “friendship paradox,” and how does it apply to influenza?

In 1991, the “friendship paradox” was born. It states that the friends of any single person are more popular than themselves. Hard pill to swallow? Well for example, take a classroom of high school students and ask each of them to name one friend. On a calculated average, the named friends of friends will live higher in the social network chain than the persons that originally named them.

Okay, okay. Still having trouble grasping the concept? Imagine a highly publicized art gallery opening in New York City with the guest of honor the focus of attention at the center of the hall. On the outskirts, a few purists lean against the walls staring at their drinks wearing glasses and bowties. You walk around and ask the invitees to each name a friend, and the results will weigh heavily in the direction of the well-connected artist. Few people will name one of the wall supporting bystanders at the gala.

The persons at the center of a social network are exposed to diseases earlier than those at the margins states the paradox. Again, your friends are probably more popular than you are, and this “friendship paradox” may help predict the spread of infectious disease. However, Christakis and Fowler found that analyzing a social network and monitoring the health of members is an optimal way to predict a wave of influenza, detailed information simply doesn’t exist for most social groups, and producing it is time-consuming and expensive.

In 2009 though, the authors of this research used the theory to analyze the 2009 flu epidemic in 744 students at Harvard University. They first reached out to 319 students who then named 425 additional friends. The friends of students, the 425, exemplified flu symptoms two weeks before the original group of 319 using one method of detection from self-reporting and data from Harvard University Health Services. In addition the “friends” group showed flu signs a full 46 days prior to the epidemic’s peak.

Christakis states:

We think this may have significant implications for public health. Public health officials often track epidemics by following random samples of people or monitoring people after they get sick. But that approach only provides a snapshot of what’s currently happening. By simply asking members of the random group to name friends, and then tracking and comparing both groups, we can predict epidemics before they strike the population at large. This would allow an earlier, more vigorous, and more effective response.

Fowler continues:

If you want a crystal ball for finding out which parts of the country are going to get the flu first, then this may be the most effective method we have now. Currently used methods are based on statistics that lag the real world – or, at best, are contemporaneous with it. We show a way you can get ahead of an epidemic of flu, or potentially anything else that spreads in networks.

Both Christakis and Fowler also think that this “friendship paradox” can be utilized across the board and foresee the spread of other diseases or even drug usage, much like social networks today spread fashion trends and gossip.

Like a third opinion? John Glasser, who was not directly involved in this study and a mathematical epidemiologist at the Centers for Disease Control in Atlanta said:

Christakis’ and Fowler’s provocative study should cause infectious disease epidemiologists and public health practitioners alike to consider the social contexts within which pathogens are transmitted. This study may be unique in demonstrating that social position affects one’s risk of acquiring disease. Consequently, epidemiologists and social scientists are modeling networks to evaluate novel disease surveillance and infection control strategies.

Christakis N, Fowler JH (2010)
“Social Network Sensors for Early Detection of Contagious Outbreaks”
PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948

Written by: Sy Kraft, B.A. – Journalism – California State University, Northridge (CSUN)