Launched in 2008, Google Flu Trends intended to combine data from the CDC with search terms containing flu-related information to estimate influenza activity in 25 countries. Researchers proposed that these search data, tuned into flu tracking information from the Centers for Disease Control and Prevention, could "nowcast" estimates of flu prevalence. However, the first version of Google Flu Trends was flawed in its data collection and modeling practices. Its methodology was to find the best matches among 50 million search terms to fit 1152 data points. Since the algorithm was too broad and the big data often over fitted the case (for instance, mistakenly identifying seasonal search terms like “high school basketball” as flu predictions) Google Flu Trends missed 2013's peak flu season by 140 percent. Subsequent revisions encountered similar problems. The project now exists as a compilation of historical estimates, though it has also inspired several other similar projects that use social media data to predict disease trends.