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Enhancing disease surveillance with novel data streams: challenges and opportunities
Journal article   Open access   Peer reviewed

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, …
EPJ data science, Vol.4(1), pp.1-8
12/01/2015
DOI: 10.1140/epjds/s13688-015-0054-0
PMCID: PMC5156315
PMID: 27990325
url
https://doi.org/10.1140/epjds/s13688-015-0054-0View
Published (Version of record) Open Access

Abstract

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.
Mathematics Physical Sciences Social Sciences Mathematical Methods In Social Sciences Mathematics, Interdisciplinary Applications Science & Technology Social Sciences, Mathematical Methods

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