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Digital disease detection: A systematic review of event-based internet biosurveillance systems
BACKGROUND: Internet access and usage has changed how people seek and report health information. Meanwhile,infectious diseases continue to threaten humanity. The analysis of Big Data, or vast digital data, presents an opportunity to improve disease surveillance and epidemic intelligence. Epidemic in...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Elsevier B.V.
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108385/ https://www.ncbi.nlm.nih.gov/pubmed/28347443 http://dx.doi.org/10.1016/j.ijmedinf.2017.01.019 |
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author | O'Shea, Jesse |
author_facet | O'Shea, Jesse |
author_sort | O'Shea, Jesse |
collection | PubMed |
description | BACKGROUND: Internet access and usage has changed how people seek and report health information. Meanwhile,infectious diseases continue to threaten humanity. The analysis of Big Data, or vast digital data, presents an opportunity to improve disease surveillance and epidemic intelligence. Epidemic intelligence contains two components: indicator based and event-based. A relatively new surveillance type has emerged called event-based Internet biosurveillance systems. These systems use information on events impacting health from Internet sources, such as social media or news aggregates. These systems circumvent the limitations of traditional reporting systems by being inexpensive, transparent, and flexible. Yet, innovations and the functionality of these systems can change rapidly. AIM: To update the current state of knowledge on event-based Internet biosurveillance systems by identifying all systems, including current functionality, with hopes to aid decision makers with whether to incorporate new methods into comprehensive programmes of surveillance. METHODS: A systematic review was performed through PubMed, Scopus, and Google Scholar databases, while also including grey literature and other publication types. RESULTS: 50 event-based Internet systems were identified, including an extraction of 15 attributes for each system, described in 99 articles. Each system uses different innovative technology and data sources to gather data, process, and disseminate data to detect infectious disease outbreaks. CONCLUSIONS: The review emphasises the importance of using both formal and informal sources for timely and accurate infectious disease outbreak surveillance, cataloguing all event-based Internet biosurveillance systems. By doing so, future researchers will be able to use this review as a library for referencing systems, with hopes of learning, building, and expanding Internet-based surveillance systems. Event-based Internet biosurveillance should act as an extension of traditional systems, to be utilised as an additional, supplemental data source to have a more comprehensive estimate of disease burden. |
format | Online Article Text |
id | pubmed-7108385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71083852020-03-31 Digital disease detection: A systematic review of event-based internet biosurveillance systems O'Shea, Jesse Int J Med Inform Article BACKGROUND: Internet access and usage has changed how people seek and report health information. Meanwhile,infectious diseases continue to threaten humanity. The analysis of Big Data, or vast digital data, presents an opportunity to improve disease surveillance and epidemic intelligence. Epidemic intelligence contains two components: indicator based and event-based. A relatively new surveillance type has emerged called event-based Internet biosurveillance systems. These systems use information on events impacting health from Internet sources, such as social media or news aggregates. These systems circumvent the limitations of traditional reporting systems by being inexpensive, transparent, and flexible. Yet, innovations and the functionality of these systems can change rapidly. AIM: To update the current state of knowledge on event-based Internet biosurveillance systems by identifying all systems, including current functionality, with hopes to aid decision makers with whether to incorporate new methods into comprehensive programmes of surveillance. METHODS: A systematic review was performed through PubMed, Scopus, and Google Scholar databases, while also including grey literature and other publication types. RESULTS: 50 event-based Internet systems were identified, including an extraction of 15 attributes for each system, described in 99 articles. Each system uses different innovative technology and data sources to gather data, process, and disseminate data to detect infectious disease outbreaks. CONCLUSIONS: The review emphasises the importance of using both formal and informal sources for timely and accurate infectious disease outbreak surveillance, cataloguing all event-based Internet biosurveillance systems. By doing so, future researchers will be able to use this review as a library for referencing systems, with hopes of learning, building, and expanding Internet-based surveillance systems. Event-based Internet biosurveillance should act as an extension of traditional systems, to be utilised as an additional, supplemental data source to have a more comprehensive estimate of disease burden. Elsevier B.V. 2017-05 2017-02-08 /pmc/articles/PMC7108385/ /pubmed/28347443 http://dx.doi.org/10.1016/j.ijmedinf.2017.01.019 Text en © 2017 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article O'Shea, Jesse Digital disease detection: A systematic review of event-based internet biosurveillance systems |
title | Digital disease detection: A systematic review of event-based internet biosurveillance systems |
title_full | Digital disease detection: A systematic review of event-based internet biosurveillance systems |
title_fullStr | Digital disease detection: A systematic review of event-based internet biosurveillance systems |
title_full_unstemmed | Digital disease detection: A systematic review of event-based internet biosurveillance systems |
title_short | Digital disease detection: A systematic review of event-based internet biosurveillance systems |
title_sort | digital disease detection: a systematic review of event-based internet biosurveillance systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108385/ https://www.ncbi.nlm.nih.gov/pubmed/28347443 http://dx.doi.org/10.1016/j.ijmedinf.2017.01.019 |
work_keys_str_mv | AT osheajesse digitaldiseasedetectionasystematicreviewofeventbasedinternetbiosurveillancesystems |