Emerging flu trends throughout the U.S. may be easier to spot than ever before by analyzing flu-related Wikipedia articles, researchers at the Boston Children’s Hospital have discovered. Through this method, the researchers have developed a way to estimate levels of flu-like illnesses throughout the country.

David McIver and John Brownstein’s model, which was published in the journal PLOS Computational Biology on Apr. 17, estimates flu levels throughout the country’s population nearly two weeks faster than data from the Centers for Disease Control and Prevention is available. The model also accurately estimates the week of highest flu activity 17 percent more often than the Google Flu Trends data.

For the study, McIver and Brownstein calculated how many times specific Wikipedia articles were accessed on a daily basis from December 2007 to August 2013. The model they developed performed commendably through flu seasons that are worse than normal and through flu-related events, such as the H1N1 pandemic in 2009.

The authors commented in the official press release, “Each influenza season provides new challenges and uncertainties to both the public as well as the public health community. We’re hoping that with this new method of influenza monitoring, we can harness publicly available data to help people get accurate, near-realtime information about the level of disease burden in the population.”

In addition, McIver told Medical Daily, “What we were really looking for was to find a way to make these estimates using data that is completely open and freely available to everybody.”