Researchers at Boston Children's Hospital said on Thursday that they may be able to track influenza-like illness in America by analyzing Internet traffic on influenza-related Wikipedia articles.
David McIver and John Brownstein created a model that accurately estimated peak influenza activity 17 percent more often than Google Flu Trends data by calculating the number of times certain Wikipedia articles were accessed. Their model predicts flu levels in the population up to two weeks earlier than the Centers for Disease Control and Prevention.
Between December 2007 and August 2013, the researchers' model performed accurately during more severe flu seasons and the H1N1 pandemic in 2009.
"Each influenza season provides new challenges and uncertainties to both the public as well as the public health community," the researchers said. "We're hoping that with this new method of influenza monitoring, we can harness publicly available data to help people get accurate, near-real time information about the level of disease burden in the population."
The researchers said the model could be used as an automatic system to support traditional surveillance methods.
The study was funded by the National Institutes of Health and the National Library of Medicine, and published in the PLOS Computational Biology journal.