Enhancing disease surveillance with novel data streams:challenges and opportunities

Althouse, Benjamin M. and Scarpino, Samuel V. and Meyers, Lauren Ancel and Ayers, John W. and Bargsten, Marisa and Baumbach, Joan and Brownstein, John S. and Castro, Lauren and Clapham, Hannah and Cummings, Derek A T and Del Valle, Sara and Eubank, Stephen and Fairchild, Geoffrey and Finelli, Lyn and Generous, Nicholas and George, Dylan and Harper, David R. and Hébert-Dufresne, Laurent and Johansson, Michael A. and Konty, Kevin and Lipsitch, Marc and Milinovich, Gabriel and Miller, Joseph D. and Nsoesie, Elaine O. and Olson, Donald R. and Paul, Michael and Polgreen, Philip M. and Priedhorsky, Reid and Read, Jonathan M. and Rodríguez-Barraquer, Isabel and Smith, Derek J. and Stefansen, Christian and Swerdlow, David L. and Thompson, Deborah and Vespignani, Alessandro and Wesolowski, Amy (2015) Enhancing disease surveillance with novel data streams:challenges and opportunities. EPJ Data Science, 4 (1). pp. 1-8. ISSN 2193-1127

Full text not available from this repository.

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.

Item Type:
Journal Article
Journal or Publication Title:
EPJ Data Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2611
Subjects:
ID Code:
78644
Deposited By:
Deposited On:
15 Aug 2016 13:22
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2020 03:22