Infoveillance

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Infoveillance is a type of syndromic surveillance that specifically utilizes information found online. [1] The term, along with the term infodemiology , was coined by Gunther Eysenbach to describe research that uses online information to gather information about human behavior. [2] [3] [4]

Contents

Eysenbach's work using Google Search queries led to the birth of Google Flu Trends, and other search engines have also been used. [5] [6] Other researchers have utilized social media sites such as Twitter to observe disease outbreak patterns. [7] [8] Infoveillance can detect disease outbreaks faster than traditional public health surveillance systems with minimal costs involved. [9]

Types

Infoveillance methods may be either passive or active. [4] Traditional infoveillance data like search engine queries and website navigation behavior are considered passive, as they attempt to recognize trends automatically, without action (or often even awareness) on the part of the internet users who are generating the data for analysis. Active infoveillance occurs when users choose to respond to a survey, enter symptoms into a website or app, or otherwise participate directly in surveillance efforts by contributing additional information. [4]

Examples

Beginning in 2008, Google used aggregated search query data to detect influenza trends and compared the results to countries' official surveillance data with the goal of predicting the spread of the flu. [10] In light of evidence that emerged in 2013 showing that Google Flu Trends sometimes substantially overestimated actual flu rates, researchers proposed a series of more advanced and better-performing approaches to flu modeling from Google search queries. [11] Google Flu Trends stopped publishing reports in 2015. [12]

Google also used aggregated search query data to detect dengue fever trends. [13] Research has also cast doubt on the accuracy of some of these predictions. [14] Google has continued this work to track and predict the COVID-19 pandemic, creating an open dataset on COVID-related search queries for use by researchers. [15]

Flu Detector

Other flu prediction projects, including Flu Detector, have come and gone since the advent and removal of Google Flu Trends. Flu Detector was developed by Vasileios Lampos and other researchers at the University of Bristol. [7] It was an application of machine learning that first used feature selection to automatically extract flu-related terms from Twitter content and then used those terms to compute a flu-score for several UK regions based on geolocated tweets. It also formed the basis for a proposed generalized scheme able to track other events. [16]

Mood of the Nation

Mood of the Nation was also developed by Lampos' team. It performed mood analysis on tweets geo-located in various regions of the United Kingdom by computing on a daily basis scores for four types of emotion: anger, fear, joy and sadness.[ citation needed ]

Privacy issues

The rise of infoveillance brings up questions about privacy. Privacy concerns are partially dependent on the level of analysis and how data are collected and managed. [4] For instance, individuals may be re-identifiable from search query datasets that have not been properly de-identified. [17] Privacy concerns are increased if data analysis is not done automatically and if search trajectories of individual users are examined. [4]

See also

References

  1. ↑ Eysenbach, Gunther (2006). "Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance". AMIA Annual Symposium Proceedings. 2006: 244–8. PMC   1839505 . PMID   17238340.
  2. ↑ Eysenbach, Gunther (2002-12-15). "Infodemiology: The epidemiology of (mis)information". The American Journal of Medicine. 113 (9): 763–765. doi: 10.1016/s0002-9343(02)01473-0 . ISSN   0002-9343. PMID   12517369.
  3. ↑ 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.
  4. 1 2 3 4 5 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.
  5. ↑ 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.
  6. ↑ 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.
  7. 1 2 Lampos, Vasileios; Cristianini, Nello (2010). "Tracking the flu pandemic by monitoring the social web". 2010 2nd International Workshop on Cognitive Information Processing. pp. 411–6. doi:10.1109/CIP.2010.5604088. ISBN   978-1-4244-6459-3. S2CID   5868871.
  8. ↑ 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. Vol. 680. pp. 559–64. doi:10.1007/978-1-4419-5913-3_61. ISBN   978-1-4419-5912-6. PMC   7123932 . PMID   20865540.
  9. ↑ 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.
  10. ↑ 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.
  11. ↑ 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. Bibcode:2015NatSR...512760L. doi:10.1038/srep12760. PMC   4522652 . PMID   26234783.
  12. ↑ Lazer, David; Kennedy, Ryan; King, Gary; Vespignani, Alessandro (2014-03-14). "The Parable of Google Flu: Traps in Big Data Analysis" (PDF). Science. 343 (6176): 1203–1205. Bibcode:2014Sci...343.1203L. doi:10.1126/science.1248506. ISSN   0036-8075. PMID   24626916. S2CID   206553739.
  13. ↑ 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.
  14. ↑ Romero-Alvarez, Daniel; Parikh, Nidhi; Osthus, Dave; Martinez, Kaitlyn; Generous, Nicholas; del Valle, Sara; Manore, Carrie A. (2020-03-26). "Google Health Trends performance reflecting dengue incidence for the Brazilian states". BMC Infectious Diseases. 20 (1): 252. doi: 10.1186/s12879-020-04957-0 . ISSN   1471-2334. PMC   7104526 . PMID   32228508.
  15. ↑ "Using symptoms search trends to inform COVID-19 research". Google. 2020-09-02. Retrieved 2021-02-15.
  16. ↑ 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.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  17. ↑ "Re-Identification of "Anonymized" Data". Georgetown Law Technology Review. 2017-04-12. Retrieved 2021-02-15.
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