Researchers at the Jean Mérieux/INSERM facility in Lyon, France, are working with the highly contagious H1N1 virus and its more lethal relation H5N1, better known as the avian flu. The scientists are attempting to determine if H1N1 could reassort with H5N1, blending their genetic material, and whether a resulting virus could have the worst traits of the original viruses.

There's a method behind creating this superflu. The facility has been working to anticipate the path current and future pandemics might take so that precautions and treatments can be developed. The team is attempting to determine when reassortment between the two viruses would produce a viable product, and which reassortments — if any — are likely to occur.

Jean Mérieux/INSERM is a biosecurity level four facility, and the researchers must wear spacesuit-like hazmat attire when in the lab, but the researchers must watch out for scratches and bites from the mice and ferrets used to test the virulence and transmissibility of the viruses.

Swine flu: One killer virus, three key questions [Nature via Metafilter]

To make its predictions accurate, researchers first looked over the data on where influenza-like illness (ILI) outbreaks took place in the U.S. over the past five years. Then they pored over search data, looking for key phrases that popped up again and again when the outbreaks occurred. You can see to the left a chart of some of the searches that correlated most strongly to a spike in ILI cases. Note that the terms listed in the chart are not exact search terms (nobody actually searches on the phrase "influenza complication"), but instead capture the idea of what people searched on. So "influenza complication" would mean people searched on things like "runny nose" or "aches and chills."

After analyzing the data, researchers came up with a system where they weighted certain search topics for how much they seemed connected to ILI outbreaks. In the chart, you can see searches on flu symptoms rank high for a correlation with hospital data on lots of flu patients. Searches on specific types of antibiotics might be connected to flu, but weren't weighted as heavily.

Eventually, they began using the list of weighted search topics against real-time data coming in during the early part of 2008, trying to see if a rise in certain topics in a given region meant that flu was on the rise there. And as you can see in the chart above, the Google prediction (in black) was uncannily similar to the actual rise in flu cases in the region they picked (in red).

So what's the advantage of the Google system of disease surveillance? Traditional methods of disease tracking rely on data from hospitals and pharmacies, and often it takes a week or more to figure out any pattern. The Google system takes about a day. If epidemiologists see a huge spike in data that points to a massive disease outbreak, they can begin to prepare for it. And more importantly, they might be able to track where the disease is heading.

What this means is that the Centers for Disease control will probably hear about the next big pandemic from Google, rather than hospital researchers. It also means that when you do a search on Google, you're revealing a lot more information about yourself than you probably realize.

Detecting Influenza Outbreaks Using Search Query Data [via Nature]