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Hi, I'm Scientific American podcast editor Steve Mirsky. And here's a short piece from the April issue of the magazine, in the section we call Advances: Dispatches from the Frontiers of Science, Technology and Medicine:
#Flu by Rachel Berkowitz
Forecasting influenza outbreaks before they strike could help officials take early action to reduce related deaths, which total 290,000 to 650,000 worldwide every year. In a recent study, researchers say they have accurately predicted outbreaks up to two weeks in advance—using only the content of social media conversations. The findings could theoretically be used to direct resources to areas that will need them most.
A team at the Pacific Northwest National Laboratory in Washington State gathered linguistic cues from Twitter conversations about seemingly non-flu-related topics such as the weather or coffee. Based on this information, the researchers nailed down when and where the next flu outbreaks were likely to occur.
The investigators used a "deep learning" computer model that mimics the layers of neurons and memory capabilities of the human brain. Their algorithm analyzed how Twitter language style, opinions and communication behaviors changed in a given period and how such changes related to later reports of flu outbreaks.
The study was published in the journal PLOS ONE.
Computer scientist Svitlana Volkova, who led the study said "the beauty of the deep-learning model we use is that it considers emotions and linguistic clues over time to predict the future." Previous efforts to forecast flu outbreaks via the Internet—including studies that used Twitter and Wikipedia records and a project called Google Flu Trends—have scanned specifically for flu-related words. In contrast, Volkova's work examined 171 million general tweets and outperformed other models that were based exclusively on word searches or clinical data suggesting an imminent outbreak.
Epidemiologist Matthew Biggerstaff of the U.S. Centers for Disease Control and Prevention cautions that we are still in "early days" when it comes to flu forecasting. But researchers are increasingly looking to the Internet to supplement official data, which are limited to a small proportion of actual cases because many infected individuals do not seek medical care. Furthermore, such a tool might one day help identify flu trends in regions where public health data are not available at all.
That was #Flu by Rachel Berkowitz.
