Cited by 819Data-Driven Disease SurveillanceSocial Media in Health EducationPharmacovigilance and Adverse Drug Reactions

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Digital disease detection - harnessing the Web for public health surveillance

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Brownstein, John S., Freifeld, Clark C., Madoff, Lawrence C.·2009·New England Journal of Medicine

DOI 10.1056/nejmp0900702PMC PMC2917042

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Abstract

The Internet has become a critical medium for clinicians, public health practitioners, and laypeople seeking health information. Data about diseases and outbreaks are disseminated not only through online announcements by government agencies but also through informal channels, ranging from press reports to blogs to chat rooms to analyses of Web searches (see Digital Resources for Disease Detection). Collectively, these sources provide a view of global health that is fundamentally different from that yielded by the disease reporting of the traditional public health infrastructure.1 Digital Resources for Disease Detection.Sample Web-based data sourcesProMED-mail, www.promedmail.orgGlobal Public Health Intelligence Network (GPHIN), . . .

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Using Internet Searches for Influenza Surveillance
Polgreen, P. M., Chen, Y., Pennock, D. M., & Nelson, F. D. (2008) · Clinical Infectious Diseases · doi:10.1086/593098 · Cited by 567
Infodemiology: tracking flu-related searches on the web for syndromic surveillance.
Eysenbach, G. (2006) · PubMed · Cited by 414
Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project
Brownstein, J. S., Freifeld, C. C., Reis, B. Y., & Mandl, K. D. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050151 · Cited by 413
ProMED-mail: An Early Warning System for Emerging Diseases
Yu, V. L., & Madoff, L. C. (2004) · Clinical Infectious Diseases · doi:10.1086/422003 · Cited by 269

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Authors: Brownstein, J. S., Freifeld, C. C., & Madoff, L. C.
Affiliations: Boston Children's Hospital (US)
ORCID: Brownstein, John S.
Freifeld, Clark C.
Madoff, Lawrence C.
Venue: New England Journal of Medicine
Publisher: Massachusetts Medical Society
Published: 2009-05-21
Volume: 360
Issue: 21
Pages: 2153-2157
ISSN: 0028-4793 (ISSN-L)
1533-4406
DOI: 10.1056/nejmp0900702
PMC: PMC2917042
OpenAlex: W2160542585
Cited by: 819
References: 5
Citations by year: 2026: 40 · 2025: 31 · 2024: 33 · 2023: 29 · 2022: 40 · 2021: 60 · 2020: 81 · 2019: 57 · 2018: 80 · 2017: 68 · 2016: 59 · 2015: 51 · 2014: 54 · 2013: 53 · 2012: 35
FWCI: 18.66
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: New England Journal of Medicine (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: U.S. National Library of Medicine (G08LM009776)
Topics: Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
Social Sciences › Health › Social Media in Health Education
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
Keywords: disease surveillance, web-based surveillance, online health information, outbreak, internet search data, ProMED-mail
MeSH terms: Disease Outbreaks, Humans, Population Surveillance, Global Health, Information Storage and Retrieval, Internet, Communicable Diseases, Emerging

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Digital disease detection - harnessing the Web for public health surveillance

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