According to a recent study published in the journal PLoS Computation Biology, researchers at Boston Children’s Hospital have found that they may be able to accurately track flu activity by analyzing internet traffic pertaining to articles regarding flu and flu like illnesses on the internet website Wikipedia.
The purpose of the study was to develop a statistical model to provide near real-time estimates of influenza like illness activity in the United States by using freely available data gathered from the internet site Wikipedia. Wikipedia is an open content, online encyclopedia written collaboratively by the people who use it. Wikipedia is currently the largest on-line encyclopedia and one of the largest websites in existence with nearly 506 million visitors per month, 27 billion total page views, and 17,800 new articles each day.
In “Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near real Time” published April 17, 2014, researchers reported that tracking the incidents of flu through Wikipedia searches yielded estimates of flu levels in the United States earlier and more accurately than other methods of tracking currently being utilized. These other methods include Google Flu Trends, a system designed by Google which uses aggregate Google search data to estimate flu activity, based on certain terms they believe to be good indicators of flu activity, and conventional tracking done by the Center for Disease Control and Prevention.
By calculating the number of times certain influenza or health related Wikipedia articles are accessed each day within a particular period, researchers were able to estimate, in near real time, the level of influenza-like illness in the United States. This method has been demonstrated to be effective at estimating the level of influenza-like illness activity in the United States up to two weeks in advance of traditional reporting. The researchers also concluded that the study “exemplifies how non-traditional data sources may be tapped to provide valuable public health related insights and, with further improvement and validation, could potentially be implemented as an automatic sentinel surveillance system for any number of disease or conditions of interest as a supplement to more traditional surveillance systems.”
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McIver DJ, Brownstein JS (2014) Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time. PLoS Comput Biol 10(4): e1003581. doi:10.1371/journal.pcbi. 1003581.