It's easy to contract a bug when you're in a public area with a lot of people. How do you predict trends like these? Adam Sadilek at the University of Rochester in New York and colleagues have applied the idea to a pile of Twitter data from people in New York City, and found that they can predict when an individual person will come down with the flu up to eight days before they show symptoms.

The idea is similar to Google Flu Trends, which tracks how often people search for "flu" and related terms on the search engine and uses that information to provide daily updates on where outbreaks and how they are spreading.

Sadilek and his team analysed 4.4 million tweets tagged with GPS location data from over 630,000 users in the New York City area over one month in 2010. A machine-learning algorithm was able to tell the difference between tweets by healthy people - who might say something like "I am so sick of this traffic!" - and someone who is actually sick and showing signs of the flu. Check out the video below.




The researchers were able to predict when healthy people were about to fall ill - and then tweet about it - with about 90 per cent accuracy out to eight days in the future.

But still, the system is limited in ways. It misses many cases of illness because people don't reliably tweet about their symptoms. And those are bed ridden, or about to get sick might not tweet their condition.

In unpublished findings described to New Scientist during an interview at the Conference on Artifical Intelligence in Toronto, Canada, yesterday, his team showed that people who go to the gym regularly are moderately less likely to get sick. People with low socio-economic status, on the other hand, are much more likely to become ill.

Could an app of such appear one day with the ability to inform you about your impending condition?