Enhancing disease surveillance with novel data streams: challenges and opportunities

Benjamin M. Althouse, Samuel V. Scarpino, Lauren Ancel Meyers, John W. Ayers, Marisa Bargsten, Joan Baumbach, John S. Brownstein, Lauren Castro, Hannah Clapham, Derek A.T. Cummings, Sara Del Valle, Stephen Eubank, Geoffrey Fairchild, Lyn Finelli, Nicholas Generous, Dylan George, David R. Harper, Laurent Hébert-Dufresne, Michael A. Johansson, Kevin KontyMarc Lipsitch, Gabriel Milinovich, Joseph D. Miller, Elaine O. Nsoesie, Donald R. Olson, Michael Paul, Philip M. Polgreen, Reid Priedhorsky, Jonathan M. Read, Isabel Rodríguez-Barraquer, Derek J. Smith, Christian Stefansen, David L. Swerdlow, Deborah Thompson, Alessandro Vespignani, Amy Wesolowski

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


Novel 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.

Original languageEnglish (US)
Article number17
Pages (from-to)1-8
Number of pages8
JournalEPJ Data Science
Issue number1
StatePublished - Dec 1 2015


  • digital surveillance
  • disease surveillance
  • novel data streams

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Computational Mathematics


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