EPJ Data Sci. (2015) 4: 17
https://doi.org/10.1140/epjds/s13688-015-0054-0

Regular Article

Enhancing disease surveillance with novel data streams: challenges and opportunities

¹ Santa Fe Institute, Santa Fe, NM, USA
² The University of Texas at Austin, Austin, TX, USA
³ San Diego State University, San Diego, CA, USA
⁴ New Mexico Department of Health, Santa Fe, NM, USA
⁵ Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, MA, USA
⁶ Department of Pediatrics, Harvard Medical School, Boston, MA, USA
⁷ Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
⁸ Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM, USA
⁹ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
¹⁰ Virginia BioInformatics Institute and Department of Population Health Sciences, Virginia Tech, Blacksburg, VA, USA
¹¹ Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
¹² Biomedical Advanced Research and Development Authority (BARDA), Assistant Secretary for Preparedness and Response (ASPR), Department of Health and Human Services, Washington, DC, USA
¹³ Chatham House, 10 St James’s Square, London, SW1Y 4LE, UK
¹⁴ Division of Vector-Borne Diseases, NCEZID, Centers for Disease Control and Prevention, San Juan, PR, USA
¹⁵ Division of Epidemiology, New York City Department of Health and Mental Hygiene, New York, NY, USA
¹⁶ Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA, USA
¹⁷ School of Population Health, The University of Queensland, Brisbane, QLD, Australia
¹⁸ Division of Vector-Borne Diseases, NCEZID, Centers for Disease Control and Prevention, Atlanta, GA, USA
¹⁹ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
²⁰ University of Iowa, Iowa City, IA, USA
²¹ Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Liverpool, CH64 7TE, UK
²² Health Protection Research Unit in Emerging and Zoonotic Infections, NIHR, Liverpool, L69 7BE, UK
²³ Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, UK
²⁴ Google Inc., Mountain View, CA, USA
²⁵ National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
²⁶ Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA

^* e-mail: althouse@santafe.edu

Received: 15 September 2015
Accepted: 8 October 2015
Published online: 16 October 2015

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.