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Infodemiology

From Wikipedia, the free encyclopedia

Infodemiology was defined by Gunther Eysenbach in the early 2000s as information epidemiology.[1] It is an area of science research focused on scanning the internet for user-contributed health-related content, with the ultimate goal of improving public health.[1][2][3] Later, it is also defined as the science of mitigating public health problems resulting from an infodemic.[4]

Origin of term

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Eysenbach first used the term in the context of measuring and predicting the quality of health information on the Web (i.e., measuring the "supply" side of information).[1] He later included in his definition methods and techniques which are designed to automatically measure and track health information "demand" (e.g., by analyzing search queries) as well as "supply" (e.g., by analyzing postings on webpages, in blogs, and news articles, for example through GPHIN) on the Internet with the overarching goal of informing public health policy and practice. In 2013, the Infovigil Project was launched in an effort to bring the research community together to help realize this goal. It is funded by the Canadian Institutes of Health Research.[5]

Eysenbach demonstrated his point by showing a correlation between flu-related searches on Google (demand data) and flu-incidence data.[2] The method is shown to be better and more timely (i.e., can predict public health events earlier) than traditional syndromic surveillance methods such as reports by sentinel physicians.[citation needed]

Application

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Researchers have applied an infodemiological approach to studying the spread of HIV/AIDS,[6] SARS,[7] especially SARS-CoV-2 [citation needed] during the COVID-19 pandemic, and influenza,[8][9][10] vaccination uptake,[11][12] antibiotics consumption,[13] the incidence of multiple sclerosis,[14][15] patterns of alcohol consumption,[16] the efficacy of using the social web for personalization of health treatment,[17][18] the contexts of status epilepticus patients,[19][20] factors of Abdominal pain and its impact on quality of life [21] and the effectiveness of the Great American Smokeout anti-smoking awareness event.[22] Applications outside the field of health care include urban planning[23] and the study of economic trends and voter preferences.[24] Infodemiology plays a role in understanding how people seek out health-related information online and how this impacts public health outcomes. As technologies that people use continues to advance, it will becomes relevant for researchers to utilize infodemiological approaches in order to stay informed about emerging health trends in the digital world. One of the main goals of infodemiology is to provide real-time information about public health trends and behaviors. By analyzing user-generated content on the internet, researchers can gain insights into people's attitudes towards health issues and track the spread of diseases or outbreaks. This information can then be used to inform public health policies and interventions. There are also challenges associated with infodemiology. One major concern is the reliability and accuracy of online information. With the rise of fake news and misinformation on the internet, it is important for researchers to carefully evaluate the data sources.[25][26][27]

Methods

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Infodemiology utilizes a variety of methods and techniques, including data mining, natural language processing, machine learning, and social network analysis. It also involves collaboration between different disciplines such as public health, computer science, sociology, and psychology.[25][26][27]

See also

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References

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  1. ^ a b c Eysenbach, Gunther (Dec 2002). "Infodemiology: The epidemiology of (mis)information". American Journal of Medicine. 113 (9): 763–5. CiteSeerX 10.1.1.8.8283. doi:10.1016/s0002-9343(02)01473-0. PMID 12517369. S2CID 35788388.
  2. ^ a b Eysenbach, G (2006). "Infodemiology: tracking flu-related searches on the web for syndromic surveillance". AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2006: 244–8. PMC 1839505. PMID 17238340.
  3. ^ Eysenbach, G (27 March 2009). "Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet". Journal of Medical Internet Research. 11 (1): e11. doi:10.2196/jmir.1157. PMC 2762766. PMID 19329408.
  4. ^ "1st WHO Infodemiology Conference". www.who.int. Retrieved 2020-11-14.
  5. ^ Eysenbach, Gunther. "The Infovigil Project". www.infodemiology.org. Archived from the original on 2017-01-23. Retrieved 2016-11-12.
  6. ^ Ling, Rebecca; Lee, Joon (2016-10-12). "Disease Monitoring and Health Campaign Evaluation Using Google Search Activities for HIV and AIDS, Stroke, Colorectal Cancer, and Marijuana Use in Canada: A Retrospective Observational Study". JMIR Public Health and Surveillance. 2 (2): e156. doi:10.2196/publichealth.6504. PMC 5081479. PMID 27733330.
  7. ^ Eysenbach, Gunther (2003-01-01). "SARS and Population Health Technology". Journal of Medical Internet Research. 5 (2): e14. doi:10.2196/jmir.5.2.e14. PMC 1550560. PMID 12857670.
  8. ^ Hansen, N. D.; Mølbak, K.; Cox, I. J.; Lioma, C. (2018). "Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation". arXiv:1802.06833 [cs.IR]. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1197-1200.
  9. ^ Woo, Hyekyung; Cho, Youngtae; Shim, Eunyoung; Lee, Jong-Koo; Lee, Chang-Gun; Kim, Seong Hwan (2016-07-04). "Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea". Journal of Medical Internet Research. 18 (7): e177. doi:10.2196/jmir.4955. ISSN 1438-8871. PMC 4949385. PMID 27377323.
  10. ^ Lampos, Vasileios; Miller, Andrew C.; Crossan, Steve; Stefansen, Christian (2015-08-03). "Advances in nowcasting influenza-like illness rates using search query logs". Scientific Reports. 5: 12760. Bibcode:2015NatSR...512760L. doi:10.1038/srep12760. ISSN 2045-2322. PMC 4522652. PMID 26234783.
  11. ^ Hansen, N. D.; Mølbak, K.; Cox, I. J.; Lioma, C. (2017). "Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries". arXiv:1702.07326 [cs.IR]. Proceedings of the 26th International Conference on World Wide Web, 773-774.
  12. ^ Hansen, N. D.; Mølbak, K.; Cox, I. J.; Lioma, C. (2016). "Ensemble Learned Vaccination Uptake Prediction using Web Search Queries". arXiv:1609.00689 [cs.IR]. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 1953-1956.
  13. ^ Hansen, N. D.; Mølbak, K.; Cox, I. J.; Lioma, C. (2018). "Predicting Antimicrobial Drug Consumption Using Web Search Data" (PDF). Proceedings of the 2018 International Conference on Digital Health. pp. 133–142. arXiv:1803.03532. Bibcode:2018arXiv180303532D. doi:10.1145/3194658.3194667. ISBN 978-1-4503-6493-5. S2CID 3801274. Archived from the original (PDF) on 2018-03-13. Retrieved 2018-03-12. Proceedings of the ACM International Conference on Digital Health 2018.
  14. ^ Bragazzi, Nicola Luigi (2013-01-01). "Infodemiology and infoveillance of multiple sclerosis in Italy". Multiple Sclerosis International. 2013: 924029. doi:10.1155/2013/924029. ISSN 2090-2654. PMC 3762202. PMID 24027636.
  15. ^ Brigo, Francesco; Lochner, Piergiorgio; Tezzon, Frediano; Nardone, Raffaele (2014-07-01). "Web search behavior for multiple sclerosis: An infodemiological study". Multiple Sclerosis and Related Disorders. 3 (4): 440–443. doi:10.1016/j.msard.2014.02.005. ISSN 2211-0356. PMID 25877054.
  16. ^ Chan, Kl; Ho, Sy; Lam, Th (2013-09-02). "Infodemiology of alcohol use in Hong Kong mentioned on blogs: infoveillance study". Journal of Medical Internet Research. 15 (9): e192. doi:10.2196/jmir.2180. ISSN 1438-8871. PMC 3785983. PMID 23999327.
  17. ^ Fernandez-Luque, Luis; Karlsen, Randi; Bonander, Jason (2011-01-01). "Review of Extracting Information From the Social Web for Health Personalization". Journal of Medical Internet Research. 13 (1): e15. doi:10.2196/jmir.1432. PMC 3221336. PMID 21278049.
  18. ^ Kim, Yoonsang; Huang, Jidong; Emery, Sherry (2016-02-26). "Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection". Journal of Medical Internet Research. 18 (2): e41. doi:10.2196/jmir.4738. ISSN 1438-8871. PMC 4788740. PMID 26920122.
  19. ^ Bragazzi, Nicola Luigi; Bacigaluppi, Susanna; Robba, Chiara; Nardone, Raffaele; Trinka, Eugen; Brigo, Francesco (2016-02-01). "Infodemiology of status epilepticus: A systematic validation of the Google Trends-based search queries". Epilepsy & Behavior. 55: 120–123. doi:10.1016/j.yebeh.2015.12.017. ISSN 1525-5069. PMID 26773681. S2CID 205757762.
  20. ^ Brigo, Francesco; Otte, Willem M.; Igwe, Stanley C.; Ausserer, Harald; Nardone, Raffaele; Tezzon, Frediano; Trinka, Eugen (2015-12-01). "Information-seeking behaviour for epilepsy: an infodemiological study of searches for Wikipedia articles". Epileptic Disorders. 17 (4): 460–466. doi:10.1684/epd.2015.0772. ISSN 1950-6945. PMID 26575365.
  21. ^ Schäfer, Florent; Faviez, Carole; Voillot, Paméla; Foulquié, Pierre; Najm, Matthieu; Jeanne, Jean-François; Fagherazzi, Guy; Schück, Stéphane; Le Nevé, Boris (2020-11-03). "Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study". Journal of Medical Internet Research. 22 (11): e17247. doi:10.2196/17247. ISSN 1438-8871. PMC 7671840. PMID 33141087.
  22. ^ Ayers, John W.; Westmaas, J. Lee; Leas, Eric C.; Benton, Adrian; Chen, Yunqi; Dredze, Mark; Althouse, Benjamin M. (2016-06-01). "Leveraging Big Data to Improve Health Awareness Campaigns: A Novel Evaluation of the Great American Smokeout". JMIR Public Health and Surveillance. 2 (1): e16. doi:10.2196/publichealth.5304. PMC 4869240. PMID 27227151.
  23. ^ "Even the most mundane online social commentary can have a purpose". The Irish Times. Retrieved 2016-11-12.
  24. ^ Blastland, Michael (2010-12-14). "What do Google, Ask and Bing search results mean?". BBC News. Retrieved 2016-11-12.
  25. ^ a b "Infodemic".
  26. ^ a b https://infodemiology.jmir.org/
  27. ^ a b "1st WHO Infodemiology Conference".

Further reading

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