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We present a machine learning-based methodology capable of providingreal-time ("nowcast") and forecast estimates of influenza activity in the US byleveraging data from multiple data sources including: Google searches, Twittermicroblogs, nearly real-time hospital visit records, and data from aparticipatory surveillance system. Our main contribution consists of combiningmultiple influenza-like illnesses (ILI) activity estimates, generatedindependently with each data source, into a single prediction of ILI utilizingmachine learning ensemble approaches. Our methodology exploits the informationin each data source and produces accurate weekly ILI predictions for up to fourweeks ahead of the release of CDC's ILI reports. We evaluate the predictiveability of our ensemble approach during the 2013-2014 (retrospective) and2014-2015 (live) flu seasons for each of the four weekly time horizons. Ourensemble approach demonstrates several advantages: (1) our ensemble method'spredictions outperform every prediction using each data source independently,(2) our methodology can produce predictions one week ahead of GFT's real-timeestimates with comparable accuracy, and (3) our two and three week forecastestimates have comparable accuracy to real-time predictions using anautoregressive model. Moreover, our results show that considerable insight isgained from incorporating disparate data streams, in the form of social mediaand crowd sourced data, into influenza predictions in all time horizons

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance