Monitoring the number of hits recorded on Wikipedia articles can help track the spread of flu and estimate levels of influenza-like illnesses faster than any existing systems, scientists said.

Researchers at Boston Children's Hospital have developed a method of estimating flu levels in the American population by analyzing Internet traffic on specific flu-related Wikipedia articles, Fox News reported.

"The model by David McIver and John Brownstein estimates flu levels up to two weeks sooner than data from the Centers for Disease Control and Prevention becomes available, and accurately estimates the week of peak influenza activity 17 percent more often than Google Flu Trends data," Press Trust of India reported.

From December 2007 to August 2013, McIver and Brownstein calculated the number of times certain Wikipedia articles were accessed every day.

"We were able to get really nice estimates of what the [flu] level is in the population," study author David McIver, a postdoctoral fellow at Boston Children's Hospital, told Fox News.

Events such as influenza seasons that are more severe than normal and H1N1 pandemic in 2009 that received high levels of media attention were performed well by the model created by them, according to PTI.

"Each influenza season provides new challenges and uncertainties to both the public as well as the public health community," 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," they added.

"We are not trying to create something that will replace the CDC or anything like that," McIver said. Rather, the researchers' goal is "to get both things to work well together, to give us a more holistic view of what is going on."

With further validation, the U.S. could use the model as an automatic system to track flu levels, providing support for traditional influenza surveillance tools, researchers said.

The study was published in the journal PLOS Computational Biology.