Literature DB >> 32310818

Infodemiology and Infoveillance: Scoping Review.

Amaryllis Mavragani1.   

Abstract

BACKGROUND: Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.
OBJECTIVE: The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.
METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.
RESULTS: Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).
CONCLUSIONS: The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research. ©Amaryllis Mavragani. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 28.04.2020.

Entities:  

Keywords:  big data; infodemiology; infoveillance; internet; review; web-based data

Year:  2020        PMID: 32310818     DOI: 10.2196/16206

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  55 in total

1.  Infodemiological study on the impact of the COVID-19 pandemic on increased headache incidences at the world level.

Authors:  Cristiana Tudor; Robert Sova
Journal:  Sci Rep       Date:  2022-06-17       Impact factor: 4.996

2.  A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study.

Authors:  Michael S Deiner; Gurbani Kaur; Stephen D McLeod; Julie M Schallhorn; James Chodosh; Daniel H Hwang; Thomas M Lietman; Travis C Porco
Journal:  J Med Internet Res       Date:  2022-07-05       Impact factor: 7.076

3.  Effect of the COVID-19 Mitigation Measure on Dental Care Needs in 17 Countries: A Regression Discontinuity Analysis.

Authors:  Xing Qu; Chenxi Yu; Qingyue He; Ziran Li; Shannon H Houser; Wei Zhang; Ding Li
Journal:  Front Public Health       Date:  2022-05-31

4.  Utilizing Baidu Index to Investigate Seasonality, Spatial Distribution and Public Attention of Dry Eye Diseases in Chinese Mainland.

Authors:  Haozhe Yu; Weizhen Zeng; Mengyao Zhang; Gezheng Zhao; Wenyu Wu; Yun Feng
Journal:  Front Public Health       Date:  2022-07-06

5.  Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs.

Authors:  Tomomi Watanabe; Shuntaro Yada; Eiji Aramaki; Hiroshi Yajima; Hayato Kizaki; Satoko Hori
Journal:  JMIR Cancer       Date:  2022-06-03

6.  Using Twitter Data for Cohort Studies of Drug Safety in Pregnancy: Proof-of-concept With β-Blockers.

Authors:  Ari Z Klein; Karen O'Connor; Lisa D Levine; Graciela Gonzalez-Hernandez
Journal:  JMIR Form Res       Date:  2022-06-30

7.  How to Fight an Infodemic: The Four Pillars of Infodemic Management.

Authors:  Gunther Eysenbach
Journal:  J Med Internet Res       Date:  2020-06-29       Impact factor: 5.428

8.  The Impact of Social Media on Panic During the COVID-19 Pandemic in Iraqi Kurdistan: Online Questionnaire Study.

Authors:  Araz Ramazan Ahmad; Hersh Rasool Murad
Journal:  J Med Internet Res       Date:  2020-05-19       Impact factor: 5.428

9.  Evaluation of Korean-Language COVID-19-Related Medical Information on YouTube: Cross-Sectional Infodemiology Study.

Authors:  Hana Moon; Geon Ho Lee
Journal:  J Med Internet Res       Date:  2020-08-12       Impact factor: 5.428

10.  Assessment of the Impact of Media Coverage on COVID-19-Related Google Trends Data: Infodemiology Study.

Authors:  Bernardo Sousa-Pinto; Aram Anto; Wienia Czarlewski; Josep M Anto; João Almeida Fonseca; Jean Bousquet
Journal:  J Med Internet Res       Date:  2020-08-10       Impact factor: 5.428

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