Virginia TechAlthouse, Benjamin M.Scarpino, Samuel V.Meyers, Lauren AncelAyers, John W.Bargsten, MarisaBaumbach, JoanBrownstein, John S.Castro, LaurenClapham, HannahCummings, Derek A. T.Del Valle, SaraEubank, StephenFairchild, GeoffreyFinelli, LynGenerous, NicholasGeorge, DylanHarper, David R.Hebert-Dufresne, LaurentJohansson, Michael A.Konty, KevinLipsitch, MarcMillinovich, GabrielMiller, Joseph D.Nsoesie, Elaine O.Olson, Donald R.Paul, MichaelPriedhorsky, ReidRead, Jonathan M.Rodriguez-Barraquer, IsabelSmith, Derek J.Stefansen, ChristianSwerdlow, David L.Thompson, DeborahVespignani, AlessandroWesolowski, AmyPolgreen, Philip M.2017-10-272017-10-272015http://hdl.handle.net/10919/79827Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.application/pdfenCreative Commons Attribution 4.0 Internationaldisease surveillancenovel data streamsdigital surveillanceArticle - RefereedThe Author(s)https://doi.org/10.1140/epjds/s13688-015-0054-0417