Infoveillance

Infoveillance is a type of syndromic surveillance, which focuses on public health related concerns, that utilizes online information and content.[1] The term was coined by Gunther Eysenbach in 2004 for the first time along with coining, Infodemiology, a new branch of science-based research.[2] [3]

The work of Gunther Eysenbach, which utilized the Google Search queries, had led to the birth of Google Flu. Other than Google search engines have also been used.[4] [5] Later other researchers utilized other social media such as Twitter to find the disease outbreak patterns.[6][7] The infoveillance detects disease outbreaks quicker than traditional public health surveillance systems with the minimal cost involved, revealing the promising results for the future surveillance methodologies.[8]

Google uses the query information to detect the flu trends and it compares the results to the countries' official surveillance data. The primary research behind the Google Flu Trend can be found in Ginsberg et al.'s work.[9] In light of evidence showing that Google Flu Trends was occasionally over-estimating flu rates, researchers have also proposed a series of more advanced and better-performing approaches to flu modeling from Google search queries.[10]

Google uses the query information to detect the dengue trends and it compares the results to the countries' official surveillance data. The primary research behind the Google Dengue Trend is found in Chan et al.'s work .[11]

Flu Detector

Flu Detector was developed by Vasileios Lampos et al. at the University of Bristol. It is an application of Machine Learning that firstly uses Feature Selection to automatically extract flu-related terms from Twitter content and then uses those terms to compute a flu-score for several UK regions based on geolocated tweets. The primary research behind the Flu Detector is found here;[6] a generalized scheme able to track other events as well is proposed here.[12]

A new, totally revamped (in terms of models and online data) version of the Flu Detector has been recently launched.

Mood of the Nation

Mood of the Nation was developed by Vasileios Lampos et al. at the University of Bristol. It performs mood analysis on tweets geo-located in various regions of the United Kingdom computing on a daily basis scores for four types of emotion: anger, fear, joy and sadness.

See also

References
  1. Eysenbach, Gunther (2006). "Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance". AMIA Annual Symposium Proceedings: 244–8. PMC 1839505. PMID 17238340.
  2. Gunther Eysenbach (May 2011). "Infodemiology and infoveillance tracking online health information and cyberbehavior for public health". American Journal of Preventive Medicine. 40 (5 Suppl 2): S154–S158. doi:10.1016/j.amepre.2011.02.006. PMID 21521589.
  3. Gunther Eysenbach (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. Domnich, Alexander; Arbuzova, Eva K.; Signori, Alessio; Amicizia, Daniela; Panatto, Donatella; Gasparini, Roberto (2014). "Demand-based web surveillance of sexually transmitted infections in Russia". International Journal of Public Health. 59 (5): 841–9. doi:10.1007/s00038-014-0581-7. PMID 25012799. S2CID 23632100.
  5. Zhou, Xi-chuan; Shen, Hai-bin (2010). "Notifiable infectious disease surveillance with data collected by search engine". Journal of Zhejiang University Science C. 11 (4): 241–8. doi:10.1631/jzus.C0910371. S2CID 31424896.
  6. Lampos, Vasileios; Cristianini, Nello (2010). "Tracking the flu pandemic by monitoring the social web". 2010 2nd International Workshop on Cognitive Information Processing: 411–6. doi:10.1109/CIP.2010.5604088. ISBN 978-1-4244-6459-3. S2CID 5868871.
  7. Corley, Courtney D.; Cook, Diane J.; Mikler, Armin R.; Singh, Karan P. (2010). "Using Web and Social Media for Influenza Surveillance". Advances in Computational Biology. Advances in Experimental Medicine and Biology. 680: 559–64. doi:10.1007/978-1-4419-5913-3_61. ISBN 978-1-4419-5912-6. PMC 7123932. PMID 20865540.
  8. Wójcik, Oktawia P; Brownstein, John S; Chunara, Rumi; Johansson, Michael A (2014-06-20). "Public health for the people: participatory infectious disease surveillance in the digital age". Emerging Themes in Epidemiology. 11 (1): 7. doi:10.1186/1742-7622-11-7. ISSN 1742-7622. PMC 4078360. PMID 24991229.
  9. Ginsberg, Jeremy; Mohebbi, Matthew H.; Patel, Rajan S.; Brammer, Lynnette; Smolinski, Mark S.; Brilliant, Larry (2008). "Detecting influenza epidemics using search engine query data". Nature. 457 (7232): 1012–4. doi:10.1038/nature07634. PMID 19020500. S2CID 125775.
  10. Lampos, Vasileios; Miller, Andrew C.; Crossan, Steve; Stefansen, Christian (3 Aug 2015). "Advances in nowcasting influenza-like illness rates using search query logs". Scientific Reports. 5 (12760): 12760. doi:10.1038/srep12760. PMC 4522652. PMID 26234783.
  11. Chan, Emily H.; Sahai, Vikram; Conrad, Corrie; Brownstein, John S. (2011). Aksoy, Serap (ed.). "Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance". PLOS Neglected Tropical Diseases. 5 (5): e1206. doi:10.1371/journal.pntd.0001206. PMC 3104029. PMID 21647308.
  12. Lampos, Vasileios, Cristianini, Nello (2012). "Nowcasting Events from the Social Web with Statistical Learning". ACM Transactions on Intelligent Systems and Technology. 3 (4): 1–22. doi:10.1145/2337542.2337557. S2CID 8297993.CS1 maint: multiple names: authors list (link)
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