“When it comes to curbing epidemics, it makes sense to understand social networks. Figuring out who might have a disease and is most likely to spread it to others is essential to controlling an outbreak.
But scientists haven’t had good ways to do that. They often rely on unrealistic models that assume all people interact with each other with equal frequency. Think of a bag of Shake ‘n Bake: chances are all the pieces of meat will be coated with equal amounts of breadcrumbs simply because they’re tossed together.
Stanford researchers Marcel Salathe and James Holland Jones have come up with a better, more strategic way to track and curb the spread of disease that reflects real-life relationships. Developing an algorithm and testing it on Facebook data, they’ve figured out how to identify the social interactions between communities — the relationships most likely to link one group to another and get more people sick.
Their “community bridge finder” algorithm is presented in a paper published in the April 8 edition of PLoS Computational Biology.
The model takes into account community structure, social networks and the fact that tightly knit groups are often connected by just a few individuals — ideas that seem obvious but have not been applied by epidemiologists.”
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Very interesting, you wouldn’t think there’s much value in a social networking site like Facebook, but when studies like this are carried out, you realise how important they can be
ummm…how does one get sick over a modem?
kind of related i guess:
http://apps.facebook.com/processors/home.php