Literature DB >> 23789639

An overview of internet biosurveillance.

D M Hartley1, N P Nelson, R R Arthur, P Barboza, N Collier, N Lightfoot, J P Linge, E van der Goot, A Mawudeku, L C Madoff, L Vaillant, R Walters, R Yangarber, J Mantero, C D Corley, J S Brownstein.   

Abstract

Internet biosurveillance utilizes unstructured data from diverse web-based sources to provide early warning and situational awareness of public health threats. The scope of source coverage ranges from local media in the vernacular to international media in widely read languages. Internet biosurveillance is a timely modality that is available to government and public health officials, healthcare workers, and the public and private sector, serving as a real-time complementary approach to traditional indicator-based public health disease surveillance methods. Internet biosurveillance also supports the broader activity of epidemic intelligence. This overview covers the current state of the field of Internet biosurveillance, and provides a perspective on the future of the field.
© 2013 The Authors Clinical Microbiology and Infection © 2013 European Society of Clinical Microbiology and Infectious Diseases.

Keywords:  Digital disease detection; Internet-based surveillance; digital epidemiology; electronic surveillance; epidemic intelligence; event-based surveillance; participatory epidemiology; web-based text mining

Mesh:

Year:  2013        PMID: 23789639     DOI: 10.1111/1469-0691.12273

Source DB:  PubMed          Journal:  Clin Microbiol Infect        ISSN: 1198-743X            Impact factor:   8.067


  33 in total

1.  Automatic detection of tweets reporting cases of influenza like illnesses in Australia.

Authors:  Guido Zuccon; Sankalp Khanna; Anthony Nguyen; Justin Boyle; Matthew Hamlet; Mark Cameron
Journal:  Health Inf Sci Syst       Date:  2015-02-24

2.  Interfacing a biosurveillance portal and an international network of institutional analysts to detect biological threats.

Authors:  Flavia Riccardo; Mika Shigematsu; Catherine Chow; C Jason McKnight; Jens Linge; Brian Doherty; Maria Grazia Dente; Silvia Declich; Mike Barker; Philippe Barboza; Laetitia Vaillant; Alastair Donachie; Abla Mawudeku; Michael Blench; Ray Arthur
Journal:  Biosecur Bioterror       Date:  2014 Nov-Dec

3.  Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise.

Authors:  Joshua Feldman; Andrea Thomas-Bachli; Jack Forsyth; Zaki Hasnain Patel; Kamran Khan
Journal:  J Am Med Inform Assoc       Date:  2019-11-01       Impact factor: 4.497

Review 4.  ProMED-mail: 22 years of digital surveillance of emerging infectious diseases.

Authors:  Malwina Carrion; Lawrence C Madoff
Journal:  Int Health       Date:  2017-05-01       Impact factor: 2.473

5.  Using social media and internet data for public health surveillance: the importance of talking.

Authors:  David M Hartley
Journal:  Milbank Q       Date:  2014-03       Impact factor: 4.911

6.  Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns.

Authors:  Michael S Deiner; Thomas M Lietman; Stephen D McLeod; James Chodosh; Travis C Porco
Journal:  JAMA Ophthalmol       Date:  2016-09-01       Impact factor: 7.389

7.  Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change.

Authors:  E E Rees; V Ng; P Gachon; A Mawudeku; D McKenney; J Pedlar; D Yemshanov; J Parmely; J Knox
Journal:  Can Commun Dis Rep       Date:  2019-05-02

8.  Google Searches and Detection of Conjunctivitis Epidemics Worldwide.

Authors:  Michael S Deiner; Stephen D McLeod; Jessica Wong; James Chodosh; Thomas M Lietman; Travis C Porco
Journal:  Ophthalmology       Date:  2019-04-11       Impact factor: 12.079

9.  Clinical Age-Specific Seasonal Conjunctivitis Patterns and Their Online Detection in Twitter, Blog, Forum, and Comment Social Media Posts.

Authors:  Michael S Deiner; Stephen D McLeod; James Chodosh; Catherine E Oldenburg; Cherie A Fathy; Thomas M Lietman; Travis C Porco
Journal:  Invest Ophthalmol Vis Sci       Date:  2018-02-01       Impact factor: 4.799

10.  Computational approaches to influenza surveillance: beyond timeliness.

Authors:  Elaine O Nsoesie; John S Brownstein
Journal:  Cell Host Microbe       Date:  2015-03-11       Impact factor: 21.023

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