Enhancing disease surveillance with novel data streams: challenges and opportunities

Benjamin M Althouse^#¹, Samuel V Scarpino^#¹, Lauren Ancel Meyers^{1

2}, John W Ayers³, Marisa Bargsten⁴, Joan Baumbach⁴, John S Brownstein^{5

6

7}, Lauren Castro⁸, Hannah Clapham⁹, Derek At Cummings⁹, Sara Del Valle⁸, Stephen Eubank¹⁰, Geoffrey Fairchild⁸, Lyn Finelli¹¹, Nicholas Generous⁸, Dylan George¹², David R Harper¹³, Laurent Hébert-Dufresne¹, Michael A Johansson¹⁴, Kevin Konty¹⁵, Marc Lipsitch¹⁶, Gabriel Milinovich¹⁷, Joseph D Miller¹⁸, Elaine O Nsoesie^{5

6}, Donald R Olson¹⁵, Michael Paul¹⁹, Philip M Polgreen²⁰, Reid Priedhorsky⁸, Jonathan M Read^{21

22}, Isabel Rodríguez-Barraquer⁹, Derek J Smith²³, Christian Stefansen²⁴, David L Swerdlow²⁵, Deborah Thompson⁴, Alessandro Vespignani²⁶, Amy Wesolowski¹⁶

Affiliations

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.

^# Contributed equally.

PMID: 27990325
PMCID: PMC5156315
DOI: 10.1140/epjds/s13688-015-0054-0

Enhancing disease surveillance with novel data streams: challenges and opportunities

Benjamin M Althouse et al. EPJ Data Sci. 2015.

. 2015;4(1):17.

doi: 10.1140/epjds/s13688-015-0054-0. Epub 2015 Oct 16.

Authors

Affiliations

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.

^# Contributed equally.

PMID: 27990325
PMCID: PMC5156315
DOI: 10.1140/epjds/s13688-015-0054-0

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.

Keywords: digital surveillance; disease surveillance; novel data streams.

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Figures

**Figure 1**
The link between public health problems and NDS is modified by user behavior (i.e., propensity to search, what terms are chosen to search, etc.), user demographics, external forces on user behavior (i.e., changing disease severity, changing press coverage, etc.), and finally by public health interventions, which by design aim to modify the public health problem creating feedback loops on the link to NDS.

See this image and copyright information in PMC

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Enhancing disease surveillance with novel data streams: challenges and opportunities

Affiliations

Enhancing disease surveillance with novel data streams: challenges and opportunities

Authors

Affiliations

Abstract

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources

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