Why does the world need the JMIR? The Internet – and more specifically, the World-Wide-Web – has an impact on many areas of medicine – broadly we can divide them into “clinical information and telemedicine”, “medical education and information exchange” and “consumer health informatics”:

• First, Internet protocols are used for clinical information and communication. In the future, Internet technology will be the platform for many telemedical applications.
• Second, the Internet revolutionizes the gathering, access and dissemination of non-clinical information in medicine: Bibliographic and factual databases are now world-wide accessible via graphical user interfaces, epidemiological and public health information can be gathered using the Internet, and increasingly the Internet is used for interactive medical education applications.
• Third, the Internet plays an important role for consumer health education, health promotion and teleprevention. (As an aside, it should be emphasized that “health education” on the Internet goes beyond the traditional model of health education, where a medical professional teaches the patient: On the Internet, much “health education” is done “consumer-to-consumer” by means of patient self support groups organizing in cyberspace. These patient-to-patient interchanges are becoming an important part of healthcare and are redefining the traditional model of preventive medicine and health promotion).

Sources:

• 
  JMIR Publications.
  https://jmirpublications.com/.
  Research Journal.
• 
  August 11, 1999. Gunther Eysenbach. “Welcome to the Journal of Medical Internet Research.” Journal of Medical Internet Research 1 (1): e649.
  https://doi.org/10.2196/jmir.1.1.e5.
  Research Journal.
• 
  August 28, 2001. G. Eysenbach. “Journal of Medical Internet Research Is Now Indexed in Medline.” Journal of Medical Internet Research 3 (3): e843.
  https://doi.org/10.2196/jmir.3.3.e25.
  Research Journal.
• 
  January 28, 2021. “Gunther Eysenbach.” In Wikipedia.
  https://en.wikipedia.org/w/index.php?title=Gunther_Eysenbach.
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  “Gunther Eysenbach.” Google Scholar.
  http://scholar.google.com/citations?user=UjKmMQcAAAAJ&hl=en.
  Reference.
• 
  “JMIR Publications – Crunchbase Company Profile & Funding.” Crunchbase.
  https://www.crunchbase.com/organization/jmir-publications.
  Financial.

Related:

• 
  TrendMD.
  https://trendmd.com/.
  Business.
• 
  “TrendMD – Crunchbase Company Profile & Funding.” Crunchbase.
  https://www.crunchbase.com/organization/trendmd.
  Financial.

Infodemiology can be defined as the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy. Infodemiology data can be collected and analyzed in near real time. Examples for infodemiology applications include: the analysis of queries from Internet search engines to predict disease outbreaks (eg. influenza); monitoring peoples’ status updates on microblogs such as Twitter for syndromic surveillance; detecting and quantifying disparities in health information availability; identifying and monitoring of public health relevant publications on the Internet (eg. anti-vaccination sites, but also news articles or expert-curated outbreak reports); automated tools to measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns. Moreover, analyzing how people search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behavior of populations.

Sources:

• 
  December 15, 2002. Gunther Eysenbach. “Infodemiology: The Epidemiology of (Mis)Information.” The American Journal of Medicine 113 (9): 763–65.
  https://doi.org/10.1016/S0002-9343(02)01473-0.
  Research Journal.
• 
  March 27, 2009. Gunther Eysenbach. “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): e1157.
  https://doi.org/10.2196/jmir.1157.
  Research Journal.
• 
  January 17, 2014. Hua Gu, Bin Chen, Honghong Zhu, Tao Jiang, Xinyi Wang, Lei Chen, Zhenggang Jiang, Dawei Zheng, and Jianmin Jiang. “Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks.” Journal of Medical Internet Research 16 (1): e2911.
  https://doi.org/10.2196/jmir.2911.
  Research Journal.
• 
  March 4, 2013. Julie M. Robillard, Louise Whiteley, Thomas Wade Johnson, Jonathan Lim, Wyeth W. Wasserman, and Judy Illes. “Utilizing Social Media to Study Information-Seeking and Ethical Issues in Gene Therapy.” Journal of Medical Internet Research 15 (3): e2313.
  https://doi.org/10.2196/jmir.2313.
  Research Journal.
• 
  “Infodemiology.” In Wikipedia.
  https://en.wikipedia.org/w/index.php?title=Infodemiology.
  Reference.

See also, on this site:

• Gunther Eysenbach founds the Journal of Medical Internet Research

Abstract

Background
Syndromic surveillance uses health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response.

Objective
While most syndromic surveillance systems rely on data from clinical encounters with health professionals, I started to explore in 2004 whether analysis of trends in Internet searches can be useful to predict outbreaks such as influenza epidemics and prospectively gathered data on Internet search trends for this purpose.

Results
There is an excellent correlation between the number of clicks on a keyword-triggered link in Google with epidemiological data from the flu season 2004/2005 in Canada (Pearson correlation coefficient of current week clicks with the following week influenza cases r=.91). The “Google ad sentinel method” proved to be more timely, more accurate and – with a total cost of Can$365.64 for the entire flu-season – considerably cheaper than the traditional method of reports on influenza-like illnesses observed in clinics by sentinel physicians.

Conclusion
Systematically collecting and analyzing health information demand data from the Internet has considerable potential to be used for syndromic surveillance. Tracking web searches on the Internet has the potential to predict population-based events relevant for public health purposes, such as real outbreaks, but may also be confounded by “epidemics of fear”. Data from such “infodemiology studies” should also include longitudinal data on health information supply.

Sources:

• 
  2006. Gunther Eysenbach. “Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance.” AMIA Annual Symposium Proceedings 2006: 244–48.
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839505/.
  National Institutes of Health.
• 
  “Public Health Surveillance.” In Wikipedia.
  https://en.wikipedia.org/w/index.php?title=Public_health_surveillance.
  Reference.

Related:

• 
  “Google Flu Trends.” In Wikipedia.
  https://en.wikipedia.org/w/index.php?title=Google_Flu_Trends.
  Reference.
  Google Flu Trends (GFT) was first launched in 2008 by Google.org to help predict outbreaks of flu. GFT stopped publishing current estimates on 9 August 2015.

“We’re not just fighting an epidemic; we’re fighting an infodemic. Fake news spreads faster and more easily than this virus, and is just as dangerous” – WHO Director-General Dr Tedros Adhanom Ghebreyesus, Munich Security Conference on Feb 15, 2020.

The World Health Organization (WHO) defines an infodemic as “an over-abundance of information, some accurate and some not that makes it hard for people to find trustworthy sources and reliable guidance when they need it.

The WHO strategy to control an infodemic includes these four pillars:

First Pillar: Facilitate Accurate Knowledge Translation
Second Pillar: Knowledge Refinement, Filtering, and Fact-Checking
Third pillar: Build eHealth Literacy
Fourth Pillar: Monitoring, Infodemiology, Infoveillance, and Social Listening

Meanwhile, scientists also monitor social media for clues about how drugs are faring.  “Evidence from the real world is valuable, as clinical trials often enroll patients who aren’t representative of the general population. We learn more about drug safety from real-world evidence and can adjust clinical recommendations to balance risk and benefits.” (WSJ)  This real-world evidence includes social media posting.  And yet, the aggressive fact-checking and censorship of anything negative to the vaccines including reports of personal experiences is removing signals that warn scientists of problems.

Sources:

• 
  February 15, 2020. Tedros Adhanom Ghebreyesus. “Munich Security Conference.” World Health Organization.
  https://www.who.int/director-general/speeches/detail/munich-security-conference.
  World Health Organization.
  This is the transcript of the speech.
• 
  February 15, 2020. “Coronavirus Disease 2019 (COVID-19) Situation Report – 86.” World Health Organization.
  https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200415-sitrep-86-covid-19.pdf.
  World Health Organization, PDF.
• 
  February 6, 2020. Matt Richtel. “W.H.O. Fights a Pandemic Besides Coronavirus: An ‘Infodemic.’” The New York Times, sec. Health.
  https://www.nytimes.com/2020/02/06/health/coronavirus-misinformation-social-media.html.
  News.
• 
  February 13, 2020. Zoe Thomas. “WHO Says Fake Coronavirus Claims Causing ‘Infodemic.’” BBC News, sec. Technology.
  https://www.bbc.com/news/technology-51497800.
  News.
• 
  February 4, 2020. Chia-Yi Hou. “WHO Warns of Coronavirus ‘infodemic’ — an Epidemic of Too Much Information.” TheHill.
  https://thehill.com/changing-america/well-being/prevention-cures/481387-were-in-an-infodemic-with-too-much-information.
  News.
• 
  June 26, 2020. Gunther Eysenbach. “How to Fight an Infodemic: The Four Pillars of Infodemic Management.” Journal of Medical Internet Research 22 (6): e21820.
  https://doi.org/10.2196/21820.
  Research Journal.
• 
  June 26, 2020. Viroj Tangcharoensathien, Neville Calleja, Tim Nguyen, Tina Purnat, Marcelo D’Agostino, Sebastian Garcia-Saiso, Mark Landry, et al. “Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation.” Journal of Medical Internet Research 22 (6): e19659.
  https://doi.org/10.2196/19659.
  Research Journal.

Related:

• 
  April 9, 2020. Jose Yunam Cuan-Baltazar, Maria José Muñoz-Perez, Carolina Robledo-Vega, Maria Fernanda Pérez-Zepeda, and Elena Soto-Vega. “Misinformation of COVID-19 on the Internet: Infodemiology Study.” JMIR Public Health and Surveillance 6 (2): e18444.
  https://doi.org/10.2196/18444.
  Research Journal.
• 
  February 15, 2021. Bin Chen, Xinyi Chen, Jin Pan, Kui Liu, Bo Xie, Wei Wang, Ying Peng, Fei Wang, Na Li, and Jianmin Jiang. “Dissemination and Refutation of Rumors During the COVID-19 Outbreak in China: Infodemiology Study.” Journal of Medical Internet Research 23 (2): e22427.
  https://doi.org/10.2196/22427.
  Research Journal.
• 
  December 15, 2020. Elaine Okanyene Nsoesie, Nina Cesare, Martin Müller, and Al Ozonoff. “COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study.” Journal of Medical Internet Research 22 (12): e24425.
  https://doi.org/10.2196/24425.
  Research Journal.
• 
  August 25, 2020. Alessandro Rovetta, and Akshaya Srikanth Bhagavathula. “Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags.” Journal of Medical Internet Research 22 (8): e20673.
  https://doi.org/10.2196/20673.
  Research Journal.
• 
  June 22, 2021. Joseph A. Ladapo and Harvey A. Risch. “Opinion | Are Covid Vaccines Riskier Than Advertised?” Wall Street Journal, sec. Opinion.
  https://www.wsj.com/articles/are-covid-vaccines-riskier-than-advertised-11624381749.
  News.

See also, on this site:

• The term “infodemiology” is coined