Enhancing disease surveillance with novel data streams: challenges and opportunities

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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.

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A study of cross-validation and bootstrap for accuracy estimation and model selection in

R Kohavi, Kohavi, Ron Kohavi (1995)

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Using internet searches for influenza surveillance.

Philip Polgreen, Yiling Chen, David Pennock … (2008)

The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.

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National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

David Broniatowski, Michael J. Paul, Mark Dredze (2013)

Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.

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Journal

Journal ID (nlm-journal-id): 101686785

Journal ID (pubmed-jr-id): 45442

Journal ID (nlm-ta): EPJ Data Sci

Journal ID (iso-abbrev): EPJ Data Sci

Title: EPJ data science

ISSN (Electronic): 2193-1127

Publication date Nihms-submitted: 13 July 2016

Publication date (Electronic): 16 October 2015

Publication date (Print): 2015

Publication date PMC-release: 14 December 2016

Volume: 4

Electronic Location Identifier: 17

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.

Author notes

[* ]Correspondence: althouse@ 123456santafe.edu ; scarpino@ 123456santafe.edu

Article

Manuscript ID: NIHMS792371

DOI: 10.1140/epjds/s13688-015-0054-0

PMC ID: 5156315

PubMed ID: 27990325

SO-VID: c8f3096a-1165-4265-8ae1-4127cb85f822

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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Enhancing disease surveillance with novel data streams: challenges and opportunities

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PLOS: COVID-19 pandemic

Most cited references 31

A study of cross-validation and bootstrap for accuracy estimation and model selection in

Using internet searches for influenza surveillance.

National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

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