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Screenshot of the spread of H7 influenza as produced by Supramap and visualized by Google Earth in 2009. This view illustrates the historical spread of high pathogenic lineages (high-altitude red lines) and the local evolution of high pathogenicity (low-altitude red lines). (Credit: Janies/OSU)

The amazing variety of data that are at our disposal enables us to get answers we never dreamt before, and even more important they allow us to formulate new questions. 

It is usual to think about how you got the flu. I was in the pub and a guy started to sneeze right by my side. I should have got infected there. May be that is a good guess, may be not.

However, we never asked where that flu virus started to affect people nor how it escaped drugs. Those where impossible answers to get, and only in a few cases, like for the swine flu, the H1N1, researchers tried to backtrack to find the origin of the virus, the place it jumped from birds to swine and from them to humans.

Now researchers, and you!, have at their disposal many more data and an application, SupraMap, that allows the rendering on a Google map of the spread and mutation of a virus.

In a study just published by Lancet Infectious Diseases, leveraging on data available on the Internet can signal the starting of an epidemics 2 weeks before the awareness raised by normal surveillance methods. The study has been conducted by researchers at the Queensland University of Technology, in Brisbane.

Researchers have been working on historical data, from the SARS and birds flu epidemics, finding that using data made available through Google Trends and Google Insights their algorithm would have resulted in a warning 2 months ahead the one given the the WHO in the case of SARS and one-two weeks before in the case of the birds flu.

At EIT ICT LABS our Partner Telecom Italia in Trento is studying how to leverage data that are continuously being produced to detect various "signs", including the ones related to the well being of the population. Most recently they have set up a Data Challenge, opening up billions of data challenging researchers to make use of them to create information. 

On the one hand we are going to see open data made available by institutions, municipalities and by a growing number of enterprises. These data will be supplemented with those made available through crowd-sourcing. All of them will become the starting point to generate applications for meaning extraction. We are just beginning but the path is quite clear.

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