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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. 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. 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. 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%). 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.

The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources' data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. This study aimed to predict the incidence of COVID-19 in Iran. Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.

To evaluate whether a digital surveillance model using Google Trends is feasible for obtaining accurate data on coronavirus disease 2019 and whether accurate predictions can be made regarding new cases. Data on total and daily new cases in each US state were collected from January 22, 2020, to April 6, 2020. Information regarding 10 keywords was collected from Google Trends, and correlation analyses were performed for individual states as well as for the United States overall. Among the 10 keywords analyzed from Google Trends, face mask, Lysol, and COVID stimulus check had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82, and 0.79, respectively. Lag and lead Pearson correlations were assessed for every state and all 10 keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states. This study documents the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as coronavirus disease 2019. This information could enable better preparation and planning of health care systems.

Background Online interest could serve as critical sentinel indicators for monitoring assisted reproductive technology (ART) demands, detecting regions with access inequities, and identifying shortcomings in policy frameworks; however, global online interest of ART has not been assessed. The study aims to investigate the spatiotemporal evolution of online interest in ART globally. Methods The study follows retrospective observational design. Data were derived from Google Trends and Relative Search Volume (RSV) data across 230 countries and regions were collected. After quality control, data from 59, 76, 69, and 61 countries and regions were included for different search terms (“Assisted reproductive technology,” “In vitro fertilization,” “Intracytoplasmic sperm injection,” and “Preimplantation genetic diagnosis,” respectively). Weekly percentage changes (WPCs) and annual percentage changes (APCs) were used to quantify the temporal variations in online interest. Results Globally, public online interest in ART demonstrated a distinct seasonal fluctuation, peaking during months 3–5 and 9–11, while declining in months 6–8 and 12 − 2. The global online interest showed a significant downward trend (APC = -11.22%, 95% Confidence Interval [CI]: -18.44 to -8.61) from 2004 to 2011, followed by a gradual increase from 2011 to 2016 (APC = 1.76%, 95% CI: -7.64 to 11.99). Interest then rapidly increased from 2016 to 2019 (APC = 27.24%, 95% CI: 12.48 to 35.20), before continuing to decline after 2020 (APC = -6.54%, 95% CI: -12.52 to -3.05) with predictions indicating further decreases by 2030. Overall, Europe exhibited relatively higher online interest, while Africa and Oceania showed lower levels. In Europe, France (RSV = 76), Italy (RSV = 66), and Spain (RSV = 32) had notable interest in ART. Whereas, in Oceania and Africa, Australia (RSV = 8), New Zealand (RSV = 6), Tunisia (RSV = 16), Algeria (RSV = 16), and Nigeria (RSV = 10) showed lower levels of interest. Israel, Réunion (France), and France ranked as the top three countries or regions with the highest online interest in ART worldwide. Conclusions Global online interest in ART shows significant variation across countries and regions, with a decline after 2020. To address these trends, ART resources and services should be allocated effectively based on seasonal and regional demand. Government action is needed to raise social awareness and improve the accessibility and utilization of ART.

Purpose There is an abundance of online information related to bariatric surgery. Patients may prefer a specific type of bariatric surgery based on what they read online. The primary aim of this study was to determine online search trends in bariatric surgery over time in Australia and worldwide. The secondary aim was to establish a relationship between public online search activity and the types of bariatric surgery performed in Australia. Materials and Method The terms “adjustable gastric band,” “sleeve gastrectomy,” and “gastric bypass surgery” were submitted for search volume analysis in Australia and worldwide using the Google Trends “Topic” search function. This was compared alongside the numbers of gastric bandings, sleeve gastrectomies, and gastric bypass surgeries performed in Australia over time to determine if there was a relationship between the two. Results Search trends for “adjustable gastric band” and “sleeve gastrectomy” in Australia were similar to trends seen worldwide. However, search trends for “gastric bypass surgery” differ between Australia and the rest of the world. It took at least a year for online searches to reflect the higher number of sleeve gastrectomies performed relative to gastric bandings. There was a lag time of over four years before online searches reflected the higher number of gastric bypass surgery performed compared to gastric banding. Conclusion Search interests in Australia and worldwide were similar for gastric banding and sleeve gastrectomy but different for gastric bypass surgery. Online search activity did not have a significant association with the types of bariatric surgery being performed in Australia. Graphical Abstract

Reporting on brucellosis, a relatively rare infectious disease caused by Brucella, is often delayed or incomplete in traditional disease surveillance systems in China. Internet search engine data related to brucellosis can provide an economical and efficient complement to a conventional surveillance system because people tend to seek brucellosis-related health information from Baidu, the largest search engine in China. In this study, brucellosis incidence data reported by the CDC of China and Baidu index data were gathered to evaluate the relationship between them. We applied an autoregressive integrated moving average (ARIMA) model and an ARIMA model with Baidu search index data as the external variable (ARIMAX) to predict the incidence of brucellosis. The two models based on brucellosis incidence data were then compared, and the ARIMAX model performed better in all the measurements we applied. Our results illustrate that Baidu index data can enhance the traditional surveillance system to monitor and predict brucellosis epidemics in China.

The Scopus, Dimensions and Web of Science databases are introducing conversational AI search. The Scopus, Dimensions and Web of Science databases are introducing conversational AI search. A hand holds a phone displaying the OpenAI website ChatGPT

IntroductionThe Internet is a widely used resource for obtaining medical information. However, the quality of information on online platforms is still debated. Our goal in this quality-controlled WebSurg® and YouTube®–based study was to compare these two online video platforms in terms of the accuracy and quality of information about sleeve gastrectomy videos.MethodsMost viewed (popular) videos returned by YouTube® search engine in response to the keyword “sleeve gastrectomy” were included in the study. The educational accuracy and quality of the videos were evaluated according to known scoring systems. A novel scoring system measured technical quality. The ten most viewed (popular) videos in WebSurg® in response to the keyword “sleeve gastrectomy” were compared with ten YouTube® videos with the highest educational/technical scores.ResultsScoring systems measuring the educational accuracy and quality of WebSurg® videos were significantly higher than ten YouTube® videos which have the most top technical scores (p < 0.05), and no significant difference was found in the assessment of ten YouTube® videos that have the highest technical ratings compared with WebSurg® videos (p 0.481).ConclusionsWebSurg® videos, which were passed through a reviewing process and were mostly prepared by academicians, remained below the expected quality. The main limitation of WebSurg® and YouTube® is the lack of information on preoperative and postoperative processes.

The geographic footprint of Lyme disease is expanding in the United States, which calls for novel methods to identify emerging endemic areas. The ubiquity of internet use coupled with the dominance of Google's search engine makes Google user search data a compelling data source for epidemiological research. We evaluated the potential of Google Health Trends to track spatiotemporal patterns in Lyme disease and identify the leading edge of disease risk in the United States. We analyzed internet search rates for Lyme disease-related queries at the designated market area (DMA) level (n = 206) for the 2011-2019 and 2020-2021 (COVID-19 pandemic) periods. We used maps and other exploratory methods to characterize changes in search behavior. To assess statistical correlation between searches and Lyme disease cases reported to Centers for Disease Control and Prevention (CDC) between 2011 and 2019, we performed a longitudinal ecological analysis with modified Poisson generalized estimating equation regression models. Mapping DMA-level changes in \"Lyme disease\" search rates revealed an expanding area of higher rates occurring along the edges of the northeastern focus of Lyme disease. Bivariate maps comparing search rates and CDC-reported incidence rates also showed a stronger than expected signal from Google Health Trends in some high-risk adjacent states such as Michigan, North Carolina, and Ohio, which may be further indication of a geographic leading edge of Lyme disease that is not fully apparent from routine surveillance. Searches for \"Lyme disease\" were a significant predictor of CDC-reported disease incidence. Each 100-unit increase in the search rate was significantly associated with a 10% increase in incidence rates (RR = 1.10, 95% CI: 1.07, 1.12) after adjusting for environmental covariates of Lyme disease identified in the literature. Google Health Trends data may help track the expansion of Lyme disease and inform the public and health care providers about emerging risks in their areas.

Telemedicine offers a multitude of potential advantages, such as enhanced health care accessibility, cost reduction, and improved patient outcomes. The significance of telemedicine has been underscored by the COVID-19 pandemic, as it plays a crucial role in maintaining uninterrupted care while minimizing the risk of viral exposure. However, the adoption and implementation of telemedicine have been relatively sluggish in certain areas. Assessing the level of interest in telemedicine can provide valuable insights into areas that require enhancement. The aim of this study is to provide a comprehensive analysis of the level of public and research interest in telemedicine from 2017 to 2022 and also consider any potential impact of the COVID-19 pandemic. Google Trends data were retrieved using the search topics \"telemedicine\" or \"e-health\" to assess public interest, geographic distribution, and trends through a joinpoint regression analysis. Bibliographic data from Scopus were used to chart publications referencing the terms \"telemedicine\" or \"eHealth\" (in the title, abstract, and keywords) in terms of scientific production, key countries, and prominent keywords, as well as collaboration and co-occurrence networks. Worldwide, telemedicine generated higher mean public interest (relative search volume=26.3%) compared to eHealth (relative search volume=17.6%). Interest in telemedicine remained stable until January 2020, experienced a sudden surge (monthly percent change=95.7%) peaking in April 2020, followed by a decline (monthly percent change=-22.7%) until August 2020, and then returned to stability. A similar trend was noted in the public interest regarding eHealth. Chile, Australia, Canada, and the United States had the greatest public interest in telemedicine. In these countries, moderate to strong correlations were evident between Google Trends and COVID-19 data (ie, new cases, new deaths, and hospitalized patients). Examining 19,539 original medical articles in the Scopus database unveiled a substantial rise in telemedicine-related publications, showing a total increase of 201.5% from 2017 to 2022 and an average annual growth rate of 24.7%. The most significant surge occurred between 2019 and 2020. Notably, the majority of the publications originated from a single country, with 20.8% involving international coauthorships. As the most productive country, the United States led a cluster that included Canada and Australia as well. European, Asian, and Latin American countries made up the remaining 3 clusters. The co-occurrence network categorized prevalent keywords into 2 clusters, the first cluster primarily focused on applying eHealth, mobile health (mHealth), or digital health to noncommunicable or chronic diseases; the second cluster was centered around the application of telemedicine and telehealth within the context of the COVID-19 pandemic. Our analysis of search and bibliographic data over time and across regions allows us to gauge the interest in this topic, offer evidence regarding potential applications, and pinpoint areas for additional research and awareness-raising initiatives.