Computational model to track flu using Twitter data

An international team led by Alessandro Vespignani from Northeastern University has developed a computational model to predict the spread of the flu in real time. This unique model uses posts on Twitter in combination with key parameters of each season’s epidemic, including the incubation period of the disease, the immunization rate, how many people an individual with the virus can infect, and the viral strains present. When tested against official influenza surveillance systems, the model has been shown to accurately(70 to 90 percent) forecast the disease’s evolution up to six weeks in advance.

The paper on the novel model received a coveted Best Paper Honorable Mention award at the 2017 International World Wide Web Conference last month following its presentation.

While the paper reports results using Twitter data, the researchers note that the model can work with data from many other digital sources, too, as well as online surveys of individuals such as influenzanet, which is very popular in Europe.

“Our model is a work in progress,” emphasizes Vespignani. “We plan to add new parameters, for example, school and workplace structure.

Adapted from press release from the Northeastern University.