Twitter-flu

2.134 exabytes (1 exabyte= one billion gigabytes) is the estimated health data that we could collect annually by 2020. This is a big amount of data with a huge potential of unveiled information: these countless spots, like puzzle pieces, if well assembled and shaped, could deliver better and more detailed diagnostic and targeted prescriptions in the very near future.

One of the most unsuspected sources might be social twittering. It has been demonstrated that compared to existing models, Twitter is able to predict flu outbreaks more rapidly (and accurately) up to six weeks ahead. Researchers form North eastern University have investigated more than 50 million tweets related to flu: this information basin contained important clues to better understand flu shot development and distribution.

Analyzing such data, could help HPs to develop more effective vaccination strategies, considering which strain was most prevalent in a region and act consequently. Furthermore physicians valuating this real-life mirror have the potential of avoiding misdiagnosis or antibiotic over-prescription.

It is known that the last-mentioned issue is a growing problem: worthy of note, the estimations reveal a 20 billion superbug cost if the healthcare community doesn’t react promptly and properly. This is definitely not the only answer for flu prevention: a developed and functional strategy will have to use different and the best available real-life data in order to guarantee better health outcomes and less unfavourable misdiagnosis.

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