Editor’s Selection IconThe other day a friend of mine bumped into some news that concerned her. She could have asked a random person about this to find out more information, but there was a bit of information that came with the news indicating that I might know more than the average person about it. So, she asked me, and as it turns out, I did not know anything. But, having heard the news from her, I noticed a different bit of information that came along with it that told me exactly who would know everything about it, so I sent along a question …. “What’s going on with the [deleted]?” I got back a message almost immediately.
If the news was a symptom of a disease, say neck pain and headache along with light sensitivity (some kind of new meningitis or something) who would my friend ask about it? Wanting to know simply if anyone around these parts had these symptoms, she might ask five or six random people, or she might ask her cousin who works in a hospital, her sister who is a cop, and her friend who is a teacher and sees lots of people on a day to day basis. Asking those three people would be more likely to produce results than a random sample of six, if the new disease was in fact afoot.
This is all obvious, and you already knew all this. And, finally, science is catching up to you!!!
A team of researchers at Harvard University are exploring the “Friendship Paradox” … a social phenomenon structurally similar to those I just described … as a tool for use in epidemiology.
Here is the paradox. For almost everyone reading this, the friends you identify are more popular than you are. Sorry, but it is true. Ask a bunch of people to name two or three friends, and on average, an objective measure of popularity or connectivity (similar things) will be higher for those named than those asked. Those asked, in a random sample, represent the average person plus or minus whatever variation there happens to be. But people are more likely to name those with higher popularity rankings, because those friends are more on the radar screen. The reported friends are a skewed, biased sample, as opposed to the randomly chosen set of individuals.
There is some evidence to suggest that disease works this way as well. The more popular individuals will actually catch the emerging diseases first. In the example I gave above, personal smarts lead to my friend (hypothetically) picking an excellent sample of potential disease informers, but she didn’t have to do that. All she had to do was to be one of a random sample of people who are asked not how they are doing (health-wise) but rather, how their friends are doing health-wise. If there is a still-rare emerging disease, it will be discovered using this method, harnessing the awesome power of the Friendship Paradox, more quickly than by taking a random sample.
(And yes, this is a little like the Placebo Effect across events rather than outcomes! Brilliant of you to notice that!)
From the study:
Current methods for the detection of contagious outbreaks ideally give contemporaneous information about the course of an epidemic, though, more typically, the indicators lag behind the epidemic … However, the situation could be improved, possibly significantly, if detection methods took advantage of a potentially informative property of social networks: during a contagious outbreak, individuals at the center of a network are likely to be infected sooner than random members of the population. Hence, the careful collection of information from a sample of central individuals within human social networks could be used to detect contagious outbreaks before they happen in the population-at-large.
So, the bad news is that your friends are more popular than you are. But this is offset by the fact that they are going to get sick and die first!!!!
Here’s a picture of a social network where you are B and your popular friend is A:
Figure 1. Network Illustrating Structural Parameters.
This real network of 105 students shows variation in structural attributes and topological position. Each circle represents a person and each line represents a friendship tie. Nodes A and B have different “degree,” a measure that indicates the number of ties. Nodes with higher degree also tend to exhibit higher “centrality” (node A with six friends is more central than B and C who both only have four friends). If contagions infect people at random at the beginning of an epidemic, central individuals are likely to be infected sooner because they lie a shorter number of steps (on average) from all other individuals in the network. Finally, although nodes B and C have the same degree, they differ in “transitivity” (the probability that any two of one’s friends are friends with each other). Node B exhibits high transitivity with many friends that know one another. In contrast, node C’s friends are not connected to one another and therefore they offer more independent possibilities for becoming infected earlier in the epidemic.
As an infection spreads, there is an exponential increase in number of infected individuals followed by a leveling and trialing off. Because of the dynamics of this process, those in more central locations in a network (your friends, the ones that are more popular than you are) will experience different timing of exposure than, say, you and the other losers (the “B’s”). Like this:
Figure 2. Theoretical expectations of differences in contagion between central individuals and the population as a whole.
This all makes sense once you understand the Friendship Paradox, and is especially interesting because it is so counter-intuitive: A good synchronous well-designed random sample sucks compared to this strange quirky approach which relies heavily on human psychology.
Christakis, N., & Fowler, J. (2010). Social Network Sensors for Early Detection of Contagious Outbreaks PLoS ONE, 5 (9) DOI: 10.1371/journal.pone.0012948