Infodemiology was defined by
Gunther Eysenbach in the early 2000s as
informationepidemiology.[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
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
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
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]
^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.
^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.
^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.
^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.
ISSN1525-5069.
PMID26773681.
S2CID205757762.
^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.
ISSN1950-6945.
PMID26575365.