The traditional approach to immunization policy (an appropriate concern in the U.S. right now, given the current panic over flu shots) typically involves trying to immunize as many people as possible in order to cut down a bug's chances of spreading throughout the population. This doesn't work all that well -- it turns out that, on average, you need to immunize 95% of the population if you're just getting a random sample. While it may be theoretically possible to immunize 95% of a population, the financial and logistical challenges are fairly daunting.
But human societies are not random. We have networks of interaction, easily demonstrated by checking out the various social software websites out there (Friendster and Tribe.net being two of the better-known ones). And when you start thinking about human behavior not as random individuals but as networks, you can come up with new ideas about immunization.
Human networks of acquaintances, computer networks like the Internet, and interacting protein networks in the body, all share a characteristic layout: most of the elements have only a few links to others, while a few individuals have a very large number of links. If one of these highly connected individuals in a human network becomes infected, she can become a "super-spreader," infecting all of her contacts and efficiently distributing the disease. This structure suggests a deceptively simple solution to the vaccination question: immunizing all the super-spreaders in a network slows or stops the spread of a disease as effectively as destroying a country's highway interchanges would stop traffic.
Reuven Cohen and colleagues at Israel's Bar-Ilan University have found that, rather than trying to immunize everyone in hopes of hitting the "super-spreaders," randomly selecting 20% of a population and asking each to name a single acquaintance, the immunizing that acquaintance, is an effective means of focusing in on those most at risk of spreading an infection to a large number of people. With a large enough population, you can even take a smaller sampling and only go after acquaintances mentioned by two or more people and still get great results.
This is a wonderful example of how thinking in terms of social networks can lead to world-changing developments.