Big Internet data isn’t out of the disease tracking scene yet. With hubris firmly in check, a team of Harvard researchers have come up with a way to tame the unruly data, combine it with other data sets, and continually calibrate it to track flu outbreaks with less error. Their new model, published Monday in the Proceedings of the National Academy of Sciences, out-performs Google Flu Trends and other models with at least double the accuracy. If the model holds up in coming flu seasons, it could reinstate some optimism in using big data to monitor disease and herald a wave of more accurate second-generation models.

arstechnica.com/...