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Purpose: The aim of the present study was to use Google Trends for evaluating the association between the internet searches of the term “conjunctivitis” and the daily new cases of COVID-19. Methods: The relative search volume (RSV) of conjunctivitis from January 1 to April 16, 2019 (control group), January 1 to April 16, 2020 (first wave), and October 1 to December 31, 2020 (second wave) was obtained using Google Trends in Italy, France, United Kingdom, and United States. The number of COVID-19 daily new cases in the same countries were retrieved from Worldometer. Lag time correlation analyses were performed to evaluate the relationship between RSV and daily new cases (Pearson’s correlation coefficient). Results: During the first wave, the lagged RSV of conjunctivitis was significantly correlated with the number of COVID-19 daily new cases in all investigated countries. The highest correlation coefficients were obtained with a lag of 16 days in Italy ( R = 0.868), 18 days in France ( R = 0.491), 15 days in United Kingdom ( R = 0.883), and 14 days in United States ( R = 0.484) (all p Conclusion: Google Trends searches on conjunctivitis were significantly correlated with COVID-19 daily new cases during the first wave in Italy, France, United Kingdom, and United States, with a lag of 14–18 days. Repeating the analysis for the second wave, however, no significant correlations were found in any of the investigated countries.

There is an information gap about the public's interest in nephrological diseases in the COVID-19 era. The objective was to identify public interest in kidney diseases during the pandemic. In this infodemiology study, Google Trends was queried for a total of 50 search queries corresponding to a broad spectrum of nephrological diseases and the term "nephrologist." Two time intervals of 2020 (March 15-July 4 and July 5-October 31) were compared to similar time intervals of 2016-2019 for providing information on interest in different phases of the pandemic. Compared to the prior 4 years, analyses showed significant decreases in relative search volume (RSV) in the majority (76%) of search queries on March 15-July 4, 2020 period. However, RSV of the majority of search queries (≈70%) on July 5-October 31, 2020 period was not significantly different from similar periods of the previous 4 years, with an increase in search terms of amyloidosis, kidney biopsy, hematuria, chronic kidney disease, hypertension, nephrolithiasis, acute kidney injury, and Fabry disease. During the early pandemic, there have been significant decreases in search volumes for many nephrological diseases. However, this trend reversed in the period from July 5 to October 31, 2020, implying the increased need for information on kidney diseases. The results of this study enable us to understand how COVID-19 impacted the interest in kidney diseases and demands/needs for kidney diseases by the general public during the pandemic.

In the midst of the COVID-19 pandemic, there have been varied responses to public health officials' recommendations about wearing face masks as a means to slow the spread of the virus. This study, by using Twitter data, aims to explore the role of digital technology in facilitating public conversations and formulating public perception regarding face masks during the COVID-19 pandemic in two contrasting contexts: the U.S. and South Korea. From January 1, 2020 to April 14, 2020, a total of 22,928 users generated 27,501 tweets regarding face masks in the U.S. network, whereas 17,267 users produced 18,686 tweets in that of South Korea. The results of the semantic network analysis shed light on Americans' initial resistance to wearing masks as well as Koreans' willingness to comply. Details of the results are discussed further in the paper. With real-time data aggregation, this study gives insight into the rising controversy regarding wearing face masks during COVID-19 while providing implications for health officials designing strategic communication messages.

During the first months of the coronavirus disease 2019 (COVID‐19) pandemic in early 2020, Google Trends data in the United States showed a strong increase in search query frequency for chest pain symptoms despite a concurrent decrease in search interest for myocardial infarction. This suggests a reduced attention to acute coronary syndrome (ACS) and chest pain as its main symptom during this time period. These observations could help explain why cardiovascular mortality rose dramatically despite a strong decrease in hospitalisation rates for ACS.

The COVID-19 pandemic is an ongoing global pandemic, which has caused global social and economic disruption. In addition to physical illness, people have to endure the intrusion of rumors psychologically. Thus, it is critical to summarize the correlating infodemic, a significant part of COVID-19, to eventually defeat the epidemic. This article aims to mine the topic distribution and evolution patterns of online rumors by comparing and contrasting COVID-19 rumors from the two most popular rumor-refuting platforms-Jiaozhen in China and Full Fact in the United Kingdom (UK)-via a novel topic mining model, text clustering based on bidirectional encoder representations from transformers (BERT), and lifecycle theory. This comparison and contrast can enrich the research of infodemiology based on the spatio-temporal aspect, providing practical guidance for governments, rumor-refuting platforms, and individuals. The comparative study highlights the similarities and differences of online rumors about global public health emergencies across countries. Copyright © 2021, IGI Global.

Purpose Temporal trends of disease-specific internet searches may provide novel insights into seasonal dynamics of disease burden and, by extension, disease pathophysiology. The aim of this study was to define the temporal trends in rhinosinusitis-specific internet searches. Methods This was a cross sectional analysis of search volume for predefined search terms. Google trends was used to explore the volume of searches for five specific search terms related to rhinosinusitis: nose, mucus, sinus, sinusitis, chronic sinusitis, which were entered into Google web search between 2004 and 2019. Results were analyzed within search “context” which included temporally associated related searches. Relative search volume (RSV) was analyzed for English and non-English speaking countries from the Northern and Southern hemispheres. Analysis of seasonality was performed using the cosinor model. Results The five specific search terms were most related to rhinosinusitis-related search contexts, indicating that they were appropriately reflective of internet queries by patients for rhinosinusitis. The RSV for rhinosinusitis-related terms and more general search terms increased with each passing year indicating constant interest in rhinosinusitis. Cosinor time series analysis revealed inquiry peaks in winter months for all five specific rhinosinusitis-related search terms independent from the hemisphere. Conclusion Over a 15-year period, Google searches with rhinosinusitis-specific search terms consistently peaked during the winter around the world. These findings indirectly support the model of viral infection or exposure as the predominant cause of acute rhinosinusitis and acute exacerbations of chronic rhinosinusitis.

Background In response to the prioritization of healthcare resources towards the COVID-19 pandemic, routine cancer screening and diagnostic have been disrupted, potentially explaining the apparent COVID-era decline in cancer cases and mortality. In this study, we identified temporal trends in public interest in cancer-related health information using the nowcasting tool Google Trends. Methods We used Google Trends to query search terms related to cancer types for short-term (September 2019–September 2020) and long-term (September 2016–September 2020) trends in the US. We compared average relative search volumes (RSV) for specified time ranges to detect recent and seasonal variation. Results General search interest declined for all cancer types beginning in March 2020, with changes in search interest for “Breast cancer,” “Colorectal cancer,” and “Melanoma” of − 30.6%, − 28.2%, and − 26.7%, respectively, and compared with the mean RSV of the two previous months. In the same time range, search interest for “Telemedicine” has increased by + 907.1% and has reached a 4-year peak with a sustained increased level of search interest. Absolute cancer mortality has declined and is presently at a 4-year low; however, search interest in cancer has been recuperating since July 2020. Conclusion We observed a marked decline in searches for cancer-related health information that mirrors the reduction in new cancer diagnoses and cancer mortality during the COVID-19 pandemic. Health professions need to be prepared for the coming demand for cancer-related healthcare, foreshadowed by recovering interest in cancer-related information on Google Trends. Supplementary Information The online version contains supplementary material available at 10.1007/s10552-021-01409-1.

Background Currently, the Internet seems to be a helpful tool for obtaining information about everything that we think about, including diseases, their prevention and treatment approaches. However, doubts exist regarding the quality and readability of such information. This study sought to assess the quality and readability of web-based Arabic information on periodontal disease. Methods In this infodemiological study, the Google, Yahoo!, and Bing search engines were searched using specific Arabic terms on periodontal disease. The first 100 consecutive websites from each engine were obtained. The eligible websites were categorized as commercial, health/professional, journalism, and other. The following tools were applied to assess the quality of the information on the included websites: the Health on the Net Foundation Code of Conduct (HONcode), the Journal of the American Medical Association (JAMA) benchmarks, and the DISCERN tool. The readability was assessed using an online readability tool. Results Of the 300 websites, 89 were eligible for quality and readability analyses. Only two websites (2.3%) were HONcode certified. Based on the DISCERN tool, 43 (48.3%) websites had low scores. The mean score of the JAMA benchmarks was 1.6 ± 1.0, but only 3 (3.4%) websites achieved “yes” responses for all four JAMA criteria. Based on the DISCERN tool, health/professional websites revealed the highest quality of information compared to other website categories. Most of the health/professional websites revealed moderate-quality information, while 55% of the commercial websites, 66% of journalism websites, and 43% of other websites showed poor quality information. Regarding readability, most of the analyzed websites presented simple and readable written content. Conclusions Aside from readable content, Arabic health information on the analyzed websites on periodontal disease is below the required level of quality.

Infodemiology is a research discipline that investigates parameters of information distribution in order to support public health and public policy. Wikipedia, a free online encyclopedia, is commonly used as a source of information for infodemiological studies. Using Pageviews analysis, we descriptively assessed the total monthly number of views of the Wikipedia articles in English describing main neurological diseases in the period from January 2018 to July 2020. Our results show a general trend of a decrease in interest in neurological disease-related pages throughout years and especially during the burst of interest towards coronavirus. The monitoring of infodemiological indicators shall be prioritized to reshape global campaigns and tailored advocacy programs.

PurposeThe purpose of the current study is to theorize and apply a socio-technological model – the powerful influence of social determinants in conditioning the effects of information attention on social outcomes. Fundamentally, this study is motivated by the idea that the social determinants of information flow can be used as a predictive tool to inform public socio-policy decisions.Design/methodology/approachThis study draws upon digital disparity literature and uses publicly available Google search queries in exploring online information attention and its relationships to the HIV/AIDS diffusion in US cities. This study’s secondary data collected from extant sources is used to draw attention to a holistic urban ecology under which online search attention represents the variation of information access at the aggregate level.FindingsThe main finding shows that online information attention, as indicated by search trend, is far from being a simple predictor, but operates in complex interactions with existing social environments. A bivariate correlation between AIDS information search and AIDS diffusion rate was found to be significant. However, predictive multivariate models displayed robust effects of social contextual variables, such as income level and racial composition of cities, in moderating the effect of online search information flow.Practical implicationsThe importance of these insights is discussed for reducing socio-health disparities at the macro-social level, and policymakers and health administrators are recommended to incubate supportive online infrastructure as an effective preventive measure at the time of a crisis.Originality/valueThe unique contribution of this study is the premise that looks at the aggregate-ecological contour of cities within which the potential benefits of information occur, instead of examining the isolated function of mediated informationper se. In this vein, online information search,in lieuof the exposure to mass media message that is often measured via self-reported items, is a particularly unique and fruitful area of future inquiry that this study promotes.

Corona Virus Disease 2019 (COVID-19) emerged in December 2019 and rapidly spread globally. Since there is still no specific treatment available, prevention of disease spread is crucial to manage the pandemic. Adequate public information is very important. To assess the optimal timing, the aim of this study was to investigate the association between web-based interest and new cases and deaths due to COVID-19. Web-based interest for queries related to ‘coronavirus’ was assessed between 1 January and 19 June 2020, using Google Trends in Australia, Brazil, Canada, Germany, Italy, South Africa, South Korea, Spain, United Kingdom, and the United States of America. Reliability analysis of the used search terms was performed using the intraclass correlation coefficient. To investigate the association between web-based interest and new COVID-19 cases or deaths, the relative search volume was analysed for correlation with new cases and deaths. Reliability analysis revealed excellent reliability for COVID-19 search terms in all countries. Web-based interest peaked between 23 February and 5 April 2020, which was prior to the peak of new infections and deaths in most included countries. There was a moderate to strong correlation between COVID-19 related queries and new cases or new deaths. Web-based interest in COVID-19 peaked prior to the peak of new infections and deaths in most countries included. Thus, monitoring public interest via Google Trends might be useful to select the optimal-timing of web-based disease-specific information and preventive measures.

Introduction and Aims To control the spread of COVID‐19, India imposed a nationwide lockdown in phases including lockdown 1.0 (25 March–14 April) and 2.0 (15 April–3 May). Among other restrictions, it involved a complete ban of alcohol sales. We aimed to examine and interpret the changes in online search interest for keywords representing different alcohol‐related themes during the lockdown period in India. Design and Methods Data were extracted using the framework described for using Google Trends in health‐related research. The list of alcohol‐related search queries was prepared for four broad themes: types of alcoholic beverages consumed; means of accessing alcohol; problems experienced due to break in alcohol supply; and help‐seeking for alcohol use disorders. The mean relative search volumes across three time periods (pre‐lockdown; lockdown 1.0; lockdown 2.0) were compared using spss version 23.0. Results A significant increase in online search interest for keywords related to the procurement of alcohol was observed in lockdown 1.0 but not during lockdown 2.0, compared with pre‐lockdown. A significant increase in online search interest for alcohol withdrawal was observed during lockdown 1.0 compared to the pre‐lockdown period. A significant increase in online search interest for keywords representing benzodiazepines was observed in lockdown 2.0. Discussion and Conclusions Indian internet users exhibited significantly increased online interest for alcohol‐related searches during lockdown. It seems that the challenges associated with offering interventions for alcohol use‐related problems are likely to continue once the lockdown is lifted and people have the option to access alcohol and treatment services freely.

Purpose: Colonoscopy is a gold standard for screening and diagnosis of colorectal cancer (CRC) The data from the search engine may reveal what information on coloscopy gains the attention of Internet users We aimed to investigate Google searches trends and terms related to colonoscopy Patients and Methods: We retrieved statistics searches related to colonoscopy using Google Trends (GT) and Google Ads (GA) for the period from April 2016 to March 2020 The GT data was used for the analysis of time and regional search patterns worldwide GA data for Australia, Canada, Ireland, New Zealand (NZ), Poland, the United Kingdom (UK), and the United States (US) were used to calculate the search volume of categories of queries related to colonoscopy Results: Globally, the relative search volume on colonoscopy has increased until the COVID-19 outbreak and revealed seasonal variation: the highest interest was observed in March (CRC awareness month), and the lowest during December (Christmas holidays) The highest number of searches per 1000 Google users-years was done in Poland (59 62) and the lowest in the UK (19 46) Most commonly, Google users searched for details on colonoscopy techniques (Australia, Canada, Ireland, NZ), anesthesia during the procedure (Poland), facility performing colonoscopy (UK, US) In all seven countries, less than 2% of queries concerned with bowel preparation before the procedure Conclusion: Before the COVID-19 pandemic, the interest in colonoscopy has increased among Google users Google users may underestimate the importance of proper bowel preparation

Online communities are playing a critical role in social communication during the COVID-19 pandemic. We reviewed 25,795 online comments from Reddit, a popular social-media website with a subsection...

Objective To assess whether web-based public inquiries into pharyngitis-related search terms follow annual incidence peaks of acute pharyngitis in various countries from both hemispheres. Methods Google Trends (GT) was utilized for systematic acquisition of pharyngitis-related search terms (sore throat, cough, fever, cold). Six countries from both hemispheres including four English (United Kingdom, United States, Canada, and Australia) and two non-English speaking countries (Austria and Germany) were selected for further analysis. Time series data on relative search interest for pharyngitis-related search terms, covering a timeframe between 2004 and 2019 were extracted. Following reliability analysis using the intra-class correlation coefficient, the cosinor time series analysis was utilized to determine annual peaks in public-inquiries. Results The extracted datasets of GT proved to be highly reliable with correlation coefficients ranging from 0.83 to 1.0. Graphical visualization showed annual seasonal peaks for pharyngitis-related search terms in all included countries. The cosinor time series analysis revealed these peaks to be statistically significant during winter months (all p Conclusion Our study revealed seasonal variations for pharyngitis-related terms which corresponded to winter incidence peaks of acute pharyngitis. These results highlight the need for easily accessible information on diagnosis, therapy, and red-flag symptoms for this common disease. Accurately informed patients might contribute to a reduction of unnecessary clinic visits and potentially cutback the futile antibiotic overuse.

Background: The Go Red for Women (GoRedW) campaign aims to increase awareness of cardiovascular disease (CVD) and stroke in women. However, assessing the effects of social campaigns on information-seeking behaviors may be challenging. The purpose of this study was to ascertain the effect of GoRedW using a large sample of unbiased real-world data from Google Trends (GTr) and evaluate the temporal correlation of online search queries for CVD and stroke in women with GoRedW. Methods: We conducted a retrospective study using GTr, a public tool from the Google search engine to obtain relative search volumes (RSVs) related to CVD and stroke in women in the period January 2004 to April 2019 in the USA. In addition, trends of GoRedW were compared with those of the well-established Breast Cancer Awareness Month (BCAM) campaign. Results: RSVs increased for queries of GoRedW and all searched terms for CVD but not for stroke in women during February compared to other months of the year without active campaign. The strong pattern with peaks of temporal increase was consistent over the 15-year study period. RSV of “Go Red for Women” in February increased on average 494% (range: 211% to 789%). The highest temporal increase on search queries for CVD was for the term “heart disease (in) women” with an average of 114% (24% to 182%). We found a positive correlation between RSVs of GoRedW and the term “heart disease (in) women” (r = 0.54, P < 0.001). RSVs for “Breast Cancer Awareness Month” had a higher increase during the active campaign month compared to GoRedW and showed a stronger positive correlation (r = 0.78, P < 0.001). Conclusions: Search engines are a valuable resource to provide insights on information demand and to assess the effectiveness of social campaigns and interventions. Our study showed an increase in the RSVs for queries of GoRedW and all CVD terms which correlated with the active campaign months over a 15-year period. Cardiol Res. 2020;11(5):348-352 doi: https://doi.org/10.14740/cr1107

Background Health-seeking behaviors change during pandemics and may increase with regard to illnesses with symptoms similar to the pandemic. The global reaction to COVID-19 may drive interest in vaccines for other diseases. Objectives Our study investigated the correlation between global online interest in COVID-19 and interest in CDC-recommended routine vaccines. Design, settings, measurements This infodemiology study used Google Trends data to quantify worldwide interest in COVID-19 and CDC-recommended vaccines using the unit search volume index (SVI), which estimates volume of online search activity relative to highest volume of searches within a specified period. SVIs from December 30, 2019 to March 30, 2020 were collected for “coronavirus (Virus)” and compared with SVIs of search terms related to CDC-recommended adult vaccines. To account for seasonal variation, we compared SVIs from December 30, 2019 to March 30, 2020 with SVIs from the same months in 2015 to 2019. We performed country-level analyses in ten COVID-19 hotspots and ten countries with low disease burden. Results There were significant positive correlations between SVIs for “coronavirus (Virus)” and search terms for pneumococcal (R = 0.89, p

Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public’s opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others., Highlights • Semantic relatedness expands the aspects for sentiment analysis. • Release a balanced gold-corpus regarding infectious diseases from Latin America. • Linguistic features outperform the accuracy of word-embeddings. • Cardinal numerals are a strong indicative of negative sentiment.

Purpose Epistaxis represents the most frequent ear, nose, throat-related emergency symptom. Seasonal variation in epistaxis incidence, with peaks during winter months, is widely accepted, although the literature itself remains inconclusive. The objective of this study was to evaluate public inquiry into nose bleeding, by considering Google-based search query frequency on “Epistaxis”-related search terms and to assess possible seasonal variations globally. Methods Epistaxis-related search terms were systematically collected and compared using Google Trends (GT). Relative search volumes for the most relevant epistaxis-related terms, covering a timeframe from 2004 to 2019 were analysed using cosinor time series analysis for the United States of America, Germany, the United Kingdom, Italy, Canada, Australia, and New Zealand. Results Graphical representation revealed seasonal variations with peaks during winter months in the majority of countries included. Subsequent cosinor analysis revealed these variations to be significant (all p Conclusion Public interest in seeking epistaxis-related information through the Internet displayed seasonal patterns in countries from both hemispheres, with the highest interest during winter months. Further studies exploring causality with environmental factors are warranted.

In December 26, 2019, 4 unusual cases of pneumonia were recorded in China, three of those were from the same family. Another 3 cases of pneumonia were found in the same hospital in December 28 and 29(1). The virus started to expand rapidly so that doctors in Wuhan Hospital reported the phenomenon to China’s CDC and WHO. The research articles began to publish online from the first of January. The Coronavirus news spread out as quickly as the virus. The virus was named new coronavirus 2019 (nCoV 2019). In January 7, 2020 the disease caused by the virus was identified as CoVID 19 (1). The altmetrics database indicates the first mentions to the peer reviewed articles about coronavirus has been tracked in social media like Twitter and Facebook since January 16,2020. The majority of mentions aggregated from the public profiles. The scientists, practitioners (like doctors) and science communicators (like journalists) were the other group of people who mentioned the scientific articles. This could be a dangerous sign for risk communication, at the same time. The public are the main group who follow the news and information on the social media but not all of the public know how to distinguish misinformation from the truth. In January 30, the coronavirus was identified as a global health threat requiring collaboration of all countries (2). In February, the virus spread out of the China and immediately became the headline of news media. In Mach 8,2020 the China health department claimed that the outbreak has been controlled and Wuhan closed makeshift hospitals(4,5) but the disease surges in Iran. The first cases of coronavirus were formally reported in 19 February 2020 in Qum city of Iran (3). At the moment, the highest rank of mortality caused by the pandemic relates to Italy, Iran, and South Korea (6). People surf the web and social media impatiently for information on cause, transmission, control and prevention of coronavirus disease (CoViD 19). They often use and re-publish the information they find online before recognizing their trustworthiness and the evidence behind them. The open access and democratic nature of health communication in social media and the Internet provide opportunity for distribution of myths and truths about coronavirus. Distribution of misinformation among public, damages public health and creates distrust to authorized sources of information. Therefore, misinformation epidemic is more dangerous than the crises. Goffman in 1964 (13) and then Eysenbach recognized and introduced the prevalence of misinformation epidemic through online health information seeking in 2002 (7). He found that most of health information that disseminate during epidemic in social media and internet disagree the evidence (8,9). He called the methods of identifying, studying and management of misinformation as infodemiology. Infodemiology progresses in crises, specially the public health crises, in which abundant of misinformation becomes accessible for public and creates complexity instead of solving the problem. WHO director in the Munich Security Conference held in 15 February doubted about distribution of misinformation about the epidemic of coronavirus and called it the infodemic by declaring that we are not fighting an epidemic, we are fighting infodemic and this requires global collaboration (10,11). In crises, public informing of the methods of evaluation of information resources is not a right strategy for tackling with dissemination of misinformation. Novel strategies are needed to tackle with new infodemic crises and to assure that right information disseminated from the trusted sources to people at the right time. We also need to guarantee that people take the right action by receiving the right information in coronavirus epidemic. Hence, the WHO’s Risk Communication Team launched a new information platform called WHO Information Network for Epidemics (EPI-WIN) (2). One of the methods that this team follows is identifying misinformation in social media, searching and finding related scientific evidence with them from sources such as scientific papers and publishing them on EPIWIN website to provide accurate information for information hunger of the people. Today, infodemic crises have become one of the priorities of health decision making authorities in the world. We also warn our society about misinformation distributed among public in the form of news, recommendation and guidelines about coronavirus. Misinformation caused more than 27 deaths by direct consumption of alcohol and detergents to avoid the risk of coronavirus in Iran (12). Recommending use of some foods, drugs and plants without scientific evidence increases rapidly among people. Some of these products like Descurainia sophia, garlic, cinnamon, nigella seeds, wild rue seeds have no evidence and some like Vitamin D have been proved to be effective by the evidence but are misinformed and distorted. These are just examples of misinformation epidemic on coronavirus 2019 pneumonic. Still there are further issues to discuss and to study about the psychological adverse effects. The spread of misinformation about coronavirus has political, industrial, economical and health related aspects which require specific strategies for treatment. It is required that a team is formed in Iran for tracking, controlling and managing the contamination of information to assess information evidence.

the Covid-19 pandemic has been marked by sudden outbreaks of SARS-CoV-2 variants harboring mutations in both the N-terminal (NTD) and receptor binding (RBD) domains of the spike protein. The goal of this study was to predict the transmissibility of SARS-CoV-2 variants from genomic sequence data.we used a target-based molecular modeling strategy combined with surface potential analysis of the NTD and RBD.we observed that both domains act synergistically to ensure optimal virus adhesion, which explains why most variants exhibit concomitant mutations in the RBD and in the NTD. Some mutation patterns affect the affinity of the spike protein for ACE-2. However, other patterns increase the electropositive surface of the spike, with determinant effects on the kinetics of virus adhesion to lipid raft gangliosides. Based on this new view of the structural dynamics of SARS-CoV-2 variants, we defined an index of transmissibility (T-index) calculated from kinetic and affinity parameters of coronavirus binding to host cells. The T-index is characteristic of each variant and predictive of its dissemination in animal and human populations.the T-index can be used as a health monitoring strategy to anticipate future Covid-19 outbreaks due to the emergence of variants of concern.

Objective Current evidence suggests a decrease in elective diagnostic imaging procedures during the COVID-19 pandemic with potentially severe long-term consequences. The aim of this study was to quantify recent trends in public interest and related online search behavior for a range of imaging modalities, and “nowcast” future scenarios with respect to imaging use. Methods We used Google Trends, a publicly available database to access search query data in systematic and quantitative fashion, to search for key terms related to clinical imaging. We queried the search volume for multiple imaging modalities, identified the most common terms, extracted data for the United States over the time range from August 1, 2016 to August 1, 2020. Results were given in relative terms, using the Google metric ‘search volume index’. Results We report a decrease in public interest across all imaging modalities since March 2020 with a subsequent slow increase starting in May 2020. Mean relative search volume (RSV) has changed by −19.4%, −38.3%, and −51.0% for the search terms “Computed tomography”, “Magnetic resonance imaging”, and “Mammography”, respectively, and comparing the two months prior to and following March 1, 2020. RSV has since steadily recuperated reaching all-year highs. Conclusion Decrease in public interest coupled with delays and deferrals of diagnostic imaging will likely result in a high demand for healthcare in the coming months. To respond to this challenge, measures such as risk-stratification algorithms must be developed to allocate resources and avoid the risk of overstraining the healthcare system.

Background: Following the COVID-19 pandemic, global interest in influenza vaccines and pneumonia vaccines has increased significantly. We aimed to examine public interest in and actual market circulation of influenza and pneumonia vaccines before and after the initial outbreak of COVID-19 and estimate the coverage and determinants of influenza and pneumonia vaccination uptake following the COVID-19 pandemic. Methods: We obtained search volume data for vaccines using the Baidu search index and collected the numbers of vaccines issued from the National Institutes for Food and Drug Control. We also conducted a cross-sectional survey among 3346 adult residents to evaluate the coverage and determinants of influenza and pneumonia vaccination uptake in the Yangtze River delta, China, from 29 January to 4 February 2021. Results: Public searches and the number of vaccines issued for the influenza vaccines and pneumonia vaccines obviously increased after the initial outbreak of COVID-19. In the total sample, 12.5% were vaccinated against influenza, and 21.5% had at least one family member vaccinated against pneumonia. A minority of participants perceived that they were highly or very highly susceptible to influenza (15.9%) and COVID-19 (6.7%). A range of socio-economic factors and perceived susceptibility to COVID-19 were associated with influenza and pneumonia vaccination uptake. Conclusions: Public interest in and issued volumes of influenza and pneumonia vaccines increased nationally following the COVID-19 pandemic. Perceptions of high susceptibility to COVID-19 were associated with the uptake of the influenza and pneumonia vaccines. Targeted interventions were needed to improve vaccination coverage. publishedVersion

Background There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P Conclusions After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.

Background Psychological health has been one of the aspects affected by the recent COVID-19 pandemic. We aim to evaluate the patterns of Google search for mental distress symptoms of Italian citizens during the various phases of the COVID-19 pandemic. Methods We assessed Google searches for psychological-health related words. We gathered and analyzed data on daily search queries on depression, anxiety, and insomnia from Google Trends, in a time ranging from the Pre-COVID phase (beginning 25th January 2020) up to the second wave phase (ending 17th October 2020). We performed three general linear models on search trends of the three words and tested whether and to what extent official data about new cases of COVID-19, information searching on new cases, and the government health measures impacted on these trends. Results Average daily search queries were higher for anxiety, followed by depression and insomnia. General linear models performed to assess differences in daily search queries for anxiety, depression and insomnia were significant, respectively [F(13, 253) = 6.80, P P P Specifically, daily search queries differed among different phases of managing the COVID-19 outbreak: anxiety [F(5, 253) = 10.35, P $$ {n}_p^2 $$ n p 2 = .17]; depression [F(5, 253) = 13.59, P $$ {n}_p^2 $$ n p 2 = .21]; insomnia [F(5, 253) = 3.52, P = .004, $$ {n}_p^2 $$ n p 2 = .07]. Conclusions Our study contributed to the investigation of online information-seeking behaviors of Italians regarding mental health throughout the entire phase of the pandemic and provides insights on the possible future trends of mental distress during upcoming pandemic phases.

Background The pandemic caused by the coronavirus disease 2019 (COVID-19) has impacted the healthcare system worldwide, leading to the suspension of elective surgeries and a decline in the utilization of minimally invasive surgeries (MIS). However, an objective parameter depicting the degree of decline of MIS is lacking. We aim to indirectly evaluate the impact of the COVID-19 pandemic on the number of MIS performed by the surgeons by evaluating the public interest in MIS using Google Trends. Methods A Google Trends search using the string ["laparoscopic" + "minimally invasive" + "robotic surgery"] was performed on June 2, 2021. The monthly relative search volume (RSV) indices were compared with the number of reported COVID-19 cases during the same period. Results RSV was highest between August 2018 and February 2020. RSV at the start of the pandemic was 95 but had declined to 51 during the first COVID-19 peak in April 2020 and 80 during the second peak in May 2021. Conclusion The monthly RSV related to MIS on Google Trends is a good tool to indirectly estimate the degree of decline in the number of MIS (both laparoscopic and robotic) performed worldwide during the pandemic.

Background The public utilize the internet as their main source for health-related information during the pandemic. This was shown by the increase in global online searches related to health during the pandemic. In this study, we described the dynamics of public interest and awareness in diabetes before and during the pandemic and determined the possible factors associated with online interest in diabetes. Methods Global online search interest for diabetes was measured using Google Trends™ database. We used the search terms “diabetes”, “type 1 diabetes”, “type 2 diabetes”, and “gestational diabetes”. We limited the results from 2010 until 2020 from all countries. Correlation between country-specific characteristics and search volume index was determined using Spearman's rank-order correlation. Results This study showed a steady increase in global online interest in diabetes during the last decade. SVI for all the diabetes search terms included in this study increased from 2019 to 2020. People searching for the term “diabetes” also searched for the different types of diabetes, causes, signs and symptoms, diagnostic tests, and treatments for diabetes. The increasing online interest in diabetes was positively correlated with percentage of individuals using the internet and the number of physicians in a country. Conclusions The results of this study showed an increasing global online interest in diabetes during the last decade. This increased global interest in diabetes should be maximized by medical doctors and public health officials in providing evidence-based information regarding prevention and control of diabetes in the internet.

Background Infodemiology is a valuable public health tool which analyses internet sources and real-time data to establish populational trends, thus assisting disease surveillance. Google trends (GT) is a popular infodemiology source widely used in previous reports to study the correlation between internet relative search volume (RSV) and surges in various pathologies. This study aims to explore the association between GT and Covid-19 symptoms and to access the possibility of predicting new surges from internet searches. Methods Individual data was extracted from GT RSV on four main Covid-19 related symptoms in the Health category (fever, headache, cough and shortness of breath) in Portugal between 2020/03/02 and 2021/02/15, corresponding to each of the identified surges of daily new cases (DNC) in Portugal, retrieved from GitHub. Pearson's-correlation coefficient was used for assessment. Additionally, a 14 days time-lag correlation analysis between data for the same period of time was performed. Results Statistically significant correlations were found between ‘fever' web searches and the DNC in the first (p = 0.02) and third (p = 0.02) wave. No statistically significant correlations were found between any other variables. Through time lag analysis, we found a maximum Pearson association between web searches for ‘cough' and the DNC during the first wave at 14 days (r = 0.55), as well as during the third wave, with a maximum association at 3 days time lag (r = 0.55). Conclusions Monitoring behaviour and public interest in health related issues, such as crisis, is necessary and may help in the establishment of better and target oriented health policies. Despite previously stated potential, constraints such as the exclusion of social media platforms or internet users' representativeness, could partly explain our limited results for portuguese predictability of new COVID-19 surges. A better understanding of GT's algorithm may lead to more detailed and precise data. Key messages Monitoring behaviour and public interest in health related issues is necessary to establish better and more targeted health policies. Google trends seems to be an helpful infodemiology source, but doesn’t allow for full representation of the population and needs better understanding.

Background An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent. Building on diverse research disciplines and expanding the discipline of infodemiology, more evidence-based interventions are needed to design infodemic management interventions and tools and implement them by health emergency responders. Objective The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics. Methods As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions. Results The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions. Conclusions Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider.

Background Massive community-wide testing has become the cornerstone of management strategies for the COVID-19 pandemic. Objective This study was a comparative analysis between the United Kingdom and China, which aimed to assess public attitudes and uptake regarding COVID-19 testing, with a focus on factors of COVID-19 testing hesitancy, including effectiveness, access, risk perception, and communication. Methods We collected and manually coded 3856 UK tweets and 9299 Chinese Sina Weibo posts mentioning COVID-19 testing from June 1 to July 15, 2020. Adapted from the World Health Organization’s 3C Model of Vaccine Hesitancy, we employed social listening analysis examining key factors of COVID-19 testing hesitancy (confidence, complacency, convenience, and communication). Descriptive analysis, time trends, geographical mapping, and chi-squared tests were performed to assess the temporal, spatial, and sociodemographic characteristics that determine the difference in attitudes or uptake of COVID-19 tests. Results The UK tweets demonstrated a higher percentage of support toward COVID-19 testing than the posts from China. There were much wider reports of public uptake of COVID-19 tests in mainland China than in the United Kingdom; however, uncomfortable experiences and logistical barriers to testing were more expressed in China. The driving forces for undergoing COVID-19 testing were personal health needs, community-wide testing, and mandatory testing policies for travel, with major differences in the ranking order between the two countries. Rumors and information inquiries about COVID-19 testing were also identified. Conclusions Public attitudes and acceptance toward COVID-19 testing constantly evolve with local epidemic situations. Policies and information campaigns that emphasize the importance of timely testing and rapid communication responses to inquiries and rumors, and provide a supportive environment for accessing tests are key to tackling COVID-19 testing hesitancy and increasing uptake.

Background COVID-19 has caused the worst international crisis since World War II. Italy was one of the countries most affected by both the pandemic and the related infodemic. The success of anti–COVID-19 strategies and future public health policies in Italy cannot separate itself from the containment of fake news and the divulgation of correct information. Objective The aim of this paper was to analyze the impact of COVID-19 on web interest in conspiracy hypotheses and risk perception of Italian web users. Methods Google Trends was used to monitor users’ web interest in specific topics, such as conspiracy hypotheses, vaccine side effects, and pollution and climate change. The keywords adopted to represent these topics were mined from Bufale.net—an Italian website specializing in detecting online hoaxes—and Google Trends suggestions (ie, related topics and related queries). Relative search volumes (RSVs) of the time-lapse periods of 2016-2020 (pre–COVID-19) and 2020-2021 (post–COVID-19) were compared through percentage difference (∆%) and the Welch t test (t). When data series were not stationary, other ad hoc criteria were used. The trend slopes were assessed through Sen slope (SS). The significance thresholds have been indicatively set at P=.05 and t=1.9. Results The COVID-19 pandemic drastically increased Italian netizens’ interest in conspiracies (∆% ∈ [60, 288], t ∈ [6, 12]). Web interest in conspiracy-related queries across Italian regions increased and became more homogeneous compared to the pre–COVID-19 period (average RSV=80±2.8, tmin=1.8, ∆min%=+12.4, min∆SD%=–25.8). In addition, a growing trend in web interest in the infodemic YouTube channel ByoBlu has been highlighted. Web interest in hoaxes has increased more than interest in antihoax services (t1=11.3 vs t2=4.5; Δ1%=+157.6 vs Δ2%=+84.7). Equivalently, web interest in vaccine side effects exceeded interest in pollution and climate change (SSvaccines=0.22, P Conclusions COVID-19 has given a significant boost to web interest in conspiracy hypotheses and has made it more uniform across regions in Italy. The pandemic accelerated an already-growing trend in users’ interest toward some fake news sources, including the 500,000-subscriber YouTube channel ByoBlu, which was removed from the platform by YouTube for disinformation in March 2021. The risk perception related to COVID-19 vaccines has been so distorted that vaccine side effect–related queries outweighed those relating to pollution and climate change, which are much more urgent issues. Moreover, a large amount of fake news has circulated about COVID-19 vaccines, remedies, and origin. Based on these findings, it is recommended that the Italian authorities implement more effective infoveillance systems, and that communication by the mass media be less sensationalistic and more consistent with the available scientific evidence. In this context, Google Trends can be used to monitor users’ response to specific infodemiological countermeasures. Further research is needed to understand the psychological mechanisms that regulate risk perception.

Background Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. Objective The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. Methods An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. Results The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). Conclusions The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

Background Premature ejaculation (PE) is one of the most described psychosocial stress and sexual complaints worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meet researchers’ specific criteria. The genuine demand from patients with regard to information on PE and related issues may thus be neglected. Objective This study aims to examine the online search trend and user demand related to PE on a national and regional scale using the dominant major search engine in mainland China. Methods The Baidu Index was queried using the PE-related terms for the period of January 2011 to December 2020. The search volume for each term was recorded to analyze the search trend and demographic distributions. For user interest, the demand and trend data were collected and analyzed. Results Of the 36 available PE search keywords, 4 PE searching topics were identified. The Baidu Search Index for each PE topic varied from 46.30% (86,840,487/187,558,154) to 6.40% (12,009,307/187,558,154). The annual percent change (APC) for the complaint topic was 48.80% (P Conclusions The fluctuating online popularity of PE searches reflects the real-time population demands. It may help medical professionals better understand population interest, population concerns, regional variations, and gender differences on a nationwide scale and make disease-specific health care policies. The internet search data could be more reliable when the insufficient and lagging registry data are completed.

Background The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)–based methods. Methods Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

BackgroundNews media coverage of antimask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views but has done little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policy makers to craft better public health messages in anticipation of poor reactions to controversial restrictions.ObjectiveUsing data from social media, this infoveillance study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (eg, business and school closures, regional lockdown differences, and additional public health restrictions, such as social distancing and masking).MethodsCOVID-19–related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 and October 31, 2020. Sentiment scores were calculated using the VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites. Dynamic regression models with autoregressive integrated moving average errors were used to examine the association between public health restrictions and changes in public opinion over time (ie, collective attention, aggregate positive sentiment, and level of disagreement), controlling for the effects of confounders (ie, daily COVID-19 case counts, holidays, and COVID-19–related official updates).ResultsIn addition to expected direct effects (eg, business closures led to decreased positive sentiment and increased disagreements), the impact of restrictions on public opinion was contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closures and other restrictions (eg, masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (ie, sentiment polarization). Partial (ie, region-targeted) lockdowns were associated with better public response (ie, higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns.ConclusionsOur study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policy makers anticipate public response to future pandemic restrictions and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.

Background Individuals frequently turning to social media to discuss medical conditions and medication, sharing their experiences and information and asking questions among themselves. These online discussions can provide valuable insights into individual perceptions of medical treatment, and increasingly, studies are focusing on the potential use of this information to improve health care management. Objective The objective of this infodemiology study was to identify social media posts mentioning paracetamol-containing products to develop a better understanding of patients’ opinions and perceptions of the drug. Methods Posts between January 2003 and March 2019 containing at least one mention of paracetamol were extracted from 18 French forums in May 2019 with the use of the Detec’t (Kap Code) web crawler. Posts were then analyzed using the automated Detec’t tool, which uses machine learning and text mining methods to inspect social media posts and extract relevant content. Posts were classified into groups: Paracetamol Only, Paracetamol and Opioids, Paracetamol and Others, and the Aggregate group. Results Overall, 44,283 posts were analyzed from 20,883 different users. Post volume over the study period showed a peak in activity between 2009 and 2012, as well as a spike in 2017 in the Aggregate group. The number of posts tended to be higher during winter each year. Posts were made predominantly by women (14,897/20,883, 71.34%), with 12.00% (2507/20,883) made by men and 16.67% (3479/20,883) by individuals of unknown gender. The mean age of web users was 39 (SD 19) years. In the Aggregate group, pain was the most common medical concept discussed (22,257/37,863, 58.78%), and paracetamol risk was the most common discussion topic, addressed in 20.36% (8902/43,725) of posts. Doliprane was the most common medication mentioned (14,058/44,283, 31.74%) within the Aggregate group, and tramadol was the most commonly mentioned drug in combination with paracetamol in the Aggregate group (1038/19,587, 5.30%). The most common unapproved indication mentioned within the Paracetamol Only group was fatigue (190/616, with 16.32% positive for an unapproved indication), with reference to dependence made by 1.61% (136/8470) of the web users, accounting for 1.33% (171/12,843) of the posts in the Paracetamol Only group. Dependence mentions in the Paracetamol and Opioids group were provided by 6.94% (248/3576) of web users, accounting for 5.44% (342/6281) of total posts. Reference to overdose was made by 245 web users across 291 posts within the Paracetamol Only group. The most common potential adverse event detected was nausea (306/12843, 2.38%) within the Paracetamol Only group. Conclusions The use of social media mining with the Detec’t tool provided valuable information on the perceptions and understanding of the web users, highlighting areas where providing more information for the general public on paracetamol, as well as other medications, may be of benefit.

Background With the prolonging of human life expectancy and subsequent population aging, osteoporosis (OP) has become an important public health issue. Objective This study aimed to understand the global public search interests and dynamic trends in “osteoporosis” using the data derived from Google Trends. Methods An online search was performed using the term “osteoporosis” in Google Trends from January 1, 2004, to December 31, 2019, under the category “Health.” Cosinor analysis was used to test the seasonality of relative search volume (RSV) for “osteoporosis.” An analysis was conducted to investigate the public search topic rising in RSV for “osteoporosis.” Results There was a descending trend of global RSV for “osteoporosis” from January 2004 to December 2014, and a slowly increasing trend from January 2015 to December 2019. Cosinor analysis showed significant seasonal variations in global RSV for “osteoporosis” (P=.01), with a peak in March and a trough in September. In addition, similar decreasing trends of RSV for “osteoporosis” were found in Australia, New Zealand, Ireland, and Canada from January 2004 to December 2019. Cosinor test revealed significant seasonal variations in RSV for “osteoporosis” in Australia, New Zealand, Canada, Ireland, UK, and USA (all P Conclusions Our study provided evidence about the public search interest and dynamic trends in OP using web-based data, which would be helpful for public health and policy making.

Background It is now common to search for health information online. A 2013 Pew Research Center survey found that 77% of online health seekers began their query at a search engine. The widespread use of online health information seeking also applies to women’s reproductive health. Despite online interest in birth control, not much is known about related interests and concerns reflected in the search terms in the United States. Objective In this study, we identify the top search terms on Google related to birth control in Louisiana and Mississippi and compare those results to the broader United States, examining how Google searches on birth control have evolved over time and identifying regional variation within states. Methods We accessed search data on birth control from 2014-2018 from 2 Google application programming interfaces (APIs), Google Trends and Google Health Trends. We selected Google as it is the most commonly used search engine. We focused our analysis on data from 2017 and compared with 2018 data as appropriate. To assess trends, we analyzed data from 2014 through 2018. To compare the relative search frequencies of the top queries across Louisiana, Mississippi, and the United States, we used the Google Health Trends API. Relative search volume by designated marketing area (DMA) gave us the rankings of search volume for each birth control method in each DMA as compared to one another. Results Results showed that when people searched for “birth control” in Louisiana and the broader United States, they were searching for information on a diverse spectrum of methods. This differs from Mississippi, where the data indicated people were mainly searching for information related to birth control pills. Across all locations, searches for birth control pills were significantly higher than any other queries related to birth control in the United States, Louisiana, and Mississippi, and this trend remained constant from 2014 to 2018. Regional level analysis showed variations in search traffic for birth control across each state. Conclusions The internet is a growing source of health information for many users, including information on birth control. Understanding popular Google search queries on birth control can inform in-person discussions initiated by family planning practitioners and broader birth control messaging campaigns. International Registered Report Identifier (IRRID) RR2-10.2196/16543

Objective Although patients with psoriasis frequently report seasonal changes in their symptoms, the seasonality of psoriasis has rarely been explored. This study aims to investigate the seasonal pattern of and global public interest in psoriasis using Google search data. Methods Internet search data were collected from Google Trends. Data on the relative search volume (RSV) from January 2004 to December 2018 were retrieved using the term psoriasis. Cosinor analyses were conducted to examine the seasonality of psoriasis using data from two southern hemisphere countries (Australia and New Zealand) and four northern hemisphere countries (USA, Canada, UK and Ireland). Results Overall, searches for psoriasis steadily decreased between 2004 and 2010, and then rose from 2011 to 2018. On cosinor analyses, RSV of ‘psoriasis’ displayed a significant seasonal variation worldwide (p Conclusion There was a significant seasonal pattern for psoriasis, with peaks in the late winter/early spring and troughs in the late summer/early autumn. Further studies are warranted to confirm the seasonal pattern of psoriasis using clinical data and to explore the underlying mechanisms.

Background The internet has made significant contributions towards health education. Analyzing the pattern of online behavior regarding meningitis and vaccinations may be worthwhile. It is hypothesized that the online search patterns in meningitis are correlated with its number of cases and the search patterns of its related vaccines. Methods This was an infodemiological study that determined the relationship among online search interest in meningitis, its worldwide number of cases and its associated vaccines. Using Google Trends™ Search Volume Indices (SVIs), we evaluated the search queries “meningitis,” “pneumococcal vaccine,” “BCG vaccine,” “meningococcal vaccine” and “influenza vaccine” in January 2021, covering January 2008 to December 2020. Spearman rank correlation was used to determine correlations between these queries. Results The worldwide search interest in meningitis from 2008 to 2020 showed an average SVI of 46 ± 8.8. The most searched topics were symptoms, vaccines, and infectious agents with SVIs of 100, 52, and 39, respectively. The top three countries with the highest search interest were Ghana, Kazakhstan, and Kenya. There were weak, but statistically significant correlations between meningitis and the BCG (ρ = 0.369, p ρ = 0.183, p Conclusion The relationships among the Google SVIs for meningitis and its related vaccines and number of cases data were inconsistent and remained unclear. Future infodemiological studies may expand their scopes to social media, semantics, and big data for more robust conclusions.

BackgroundThere is a limited amount of data on the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V) safety profile. Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs.ObjectiveWe aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs.Materials and MethodsWe collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multi-label classifications using the deep neural language model BERT “DeepPavlov”, which we pre-trained on a Russian language corpus and applied to the Telegram messages. The resulting AUC score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea.ResultsThe results of the retrospective analysis showed that females reported more AEs than males (1.2-fold, Pβ = .05 per year, PThe Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=.94, P=.02) with those reported in the Argentinian post-marketing AE registry.ConclusionAfter receiving the Sputnik V vaccination, Telegram users complained about pain (47%), fever (47%), fatigue (34%), and headache (25%). The results showed that the AE profile of Sputnik V was comparable with other COVID-19 vaccines. Examining the sentinel properties of participatory trials (which is subject to self-reporting biases) could still provide meaningful information about pharmaceutics, especially if only a limited amount of information on AEs is provided by producers.

Background The current COVID-19 pandemic is associated with extensive individual and societal challenges, including challenges to both physical and mental health. To date, the development of mental health problems such as depressive symptoms accompanying population-based federal distancing measures is largely unknown, and opportunities for rapid, effective, and valid monitoring are currently a relevant matter of investigation. Objective In this study, we aim to investigate, first, the temporal progression of depressive symptoms during the COVID-19 pandemic and, second, the consistency of the results from tweets and survey-based self-reports of depressive symptoms within the same time period. Methods Based on a cross-sectional population survey of 9011 German adolescents and adults (n=4659, 51.7% female; age groups from 15 to 50 years and older) and a sample of 88,900 tweets (n=74,587, 83.9% female; age groups from 10 to 50 years and older), we investigated five depressive symptoms (eg, depressed mood and energy loss) using items from the Patient Health Questionnaire (PHQ-8) before, during, and after relaxation of the first German social contact ban from January to July 2020. Results On average, feelings of worthlessness were the least frequently reported symptom (survey: n=1011, 13.9%; Twitter: n=5103, 5.7%) and fatigue or loss of energy was the most frequently reported depressive symptom (survey: n=4472, 51.6%; Twitter: n=31,005, 34.9%) among both the survey and Twitter respondents. Young adult women and people living in federal districts with high COVID-19 infection rates were at an increased risk for depressive symptoms. The comparison of the survey and Twitter data before and after the first contact ban showed that German adolescents and adults had a significant decrease in feelings of fatigue and energy loss over time. The temporal progression of depressive symptoms showed high correspondence between both data sources (ρ=0.76-0.93; P Conclusions Overall, the results indicate relatively small differences in depressive symptoms associated with social distancing measures during the COVID-19 pandemic and highlight the need to differentiate between positive (eg, energy level) and negative (eg, depressed mood) associations and variations over time. The results also underscore previous suggestions of Twitter data’s potential to help identify hot spots of declining and improving public mental health and thereby help provide early intervention measures, especially for young and middle-aged adults. Further efforts are needed to investigate the long-term consequences of recurring lockdown phases and to address the limitations of social media data such as Twitter data to establish real-time public mental surveillance approaches.

Backgroundreducing the spread and impact epidemics and pandemics requires that members of the general population change their behaviors according to the recommendations, restrictions and laws provided by leading authorities. When a new epidemic or pandemic emerges, people are faced with the challenge of sorting through a great volume of varied information. Therefore, the dissemination of high-quality web-based information is essential during this time period. The overarching aim was to investigate the quality of web-based information about preventive measures and self care methods at the beginning of the COVID-19 pandemic.Methodsin May 2020, consumer-oriented websites written in Swedish were identified via systematic searches in Google (n = 76). Websites were assessed with inductive content analysis, the JAMA benchmarks, the QUEST tool and the DISCERN instrument.Resultsseven categories and 33 subcategories were identified concerning preventive measures (md = 6.0 subcategories), with few specifying a method for washing hands (n = 4), when to sanitize the hands (n = 4), and a method for sanitizing the hands (n = 1). Eight categories and 30 subcategories were identified concerning self care methods (md = 3.0 subcategories), with few referring to the national number for telephone-based counseling (n = 20) and an online symptom assessment tool (n = 16). Overall, the median total quality scores were low (JAMA = 0/4, QUEST =13/28, DISCERN = 29/80).Conclusionsat the beginning of the pandemic, substantial quality deficits of websites about COVID-19 may have counteracted the public recommendations for preventive measures. This illustrates a critical need for standardized and systematic routines on how to achieve dissemination of high-quality web-based information when new epidemics and pandemics emerge.

Background The data retrieved with the online search engine, Google Trends, can summarize internet inquiries into specified search terms. This engine may be used for analyzing inquiry peaks for different medical conditions and symptoms. Objective The aim of this study was to analyze World Wide Web interest peaks for “ear pain,” “ear infection,” and “ear drops.” Methods We used Google Trends to assess the public online interest for search terms “ear pain,” “ear infection,” and “ear drops” in 5 English and non–English-speaking countries from both hemispheres based on time series data. We performed our analysis for the time frame between January 1, 2004, and December 31, 2019. First, we assessed whether our search terms were most relevant to the topics of ear pain, ear infection, and ear drops. We then tested the reliability of Google Trends time series data using the intraclass correlation coefficient. In a second step, we computed univariate time series plots to depict peaks in web-based interest. In the last step, we used the cosinor analysis to test the statistical significance of seasonal interest peaks. Results In the first part of the study, it was revealed that “ear infection,” “ear pain,” and “ear drops” were the most relevant search terms in the noted time frame. Next, the intraclass correlation analysis showed a moderate to excellent reliability for all 5 countries’ 3 primary search terms. The subsequent analysis revealed winter interest peaks for “ear infection” and “ear pain”. On the other hand, the World Wide Web search for “ear drops” peaked annually during the summer months. All peaks were statistically significant as revealed by the cosinor model (all P values Conclusions It can be concluded that individuals affected by otitis media or externa, possibly the majority, look for medical information online. Therefore, there is a need for accurate and easily accessible information on these conditions in the World Wide Web, particularly on differentiating signs and therapy options. Meeting this need may facilitate timely diagnosis, proper therapy, and eventual circumvention of potentially life-threatening complications.

The COVID-19 pandemic and the related infodemic generated confusion and increased demand of various pharmaceuticals, ushering in the opportunity for illicit online vendors to fill a gap in the marketplace using potentially dangerous products. The aim of our study is to provide evidence regarding increased demand, online availability and consumer accessibility of ivermectin, an anthelmintic agent, without substantiated indications in reference to SARS-CoV-2. In our study, we combined infodemiology methodology aligned with search engine result assessment and website analytics to evaluate patient safety risks. Users’ Google queries regarding ivermectin were trending and peaked during the last week of November 2020 and March 2021. Consumers more likely found links leading directly or indirectly (via redirection) to illegal online retailers representing nearly half (53.3%) of search engine result links regarding the first three result pages in December 2020 and topped off at 73.3% by March 2021. Illicit medicine retailers outnumbered and outranked their legitimate counterparts and dominated the first search engine results page. A vast majority (77.7%) of the identified online pharmacies were characteristically rogue, more than half (55.5%) offered prescription-only products without a valid medical prescription. Our results illustrate connection between infodemic and its consequences on the illicit online pharmacy market

Background The COVID-19 pandemic has necessitated a rapid shift in how individuals interact with and receive fundamental services, including health care. Although telemedicine is not a novel technology, previous studies have offered mixed opinions surrounding its utilization. However, there exists a dearth of research on how these opinions have evolved over the course of the current pandemic. Objective This study aims to evaluate how the language and sentiment surrounding telemedicine has evolved throughout the COVID-19 pandemic. Methods Tweets published between January 1, 2020, and April 24, 2021, containing at least one telemedicine-related and one COVID-19–related search term (“telemedicine-COVID”) were collected from the Twitter full archive search (N=351,718). A comparator sample containing only COVID-19 terms (“general-COVID”) was collected and sampled based on the daily distribution of telemedicine-COVID tweets. In addition to analyses of retweets and favorites, sentiment analysis was performed on both data sets in aggregate and within a subset of tweets receiving the top 100 most and least retweets. Results Telemedicine gained prominence during the early stages of the pandemic (ie, March through May 2020) before leveling off and reaching a steady state from June 2020 onward. Telemedicine-COVID tweets had a 21% lower average number of retweets than general-COVID tweets (incidence rate ratio 0.79, 95% CI 0.63-0.99; P=.04), but there was no difference in favorites. A majority of telemedicine-COVID tweets (180,295/351,718, 51.3%) were characterized as “positive,” compared to only 38.5% (135,434/351,401) of general-COVID tweets (P Conclusions During the COVID-19 pandemic, opinions surrounding telemedicine evolved to become more positive, especially when compared to the larger pool of COVID-19–related tweets. Decision makers should capitalize on these shifting public opinions to invest in telemedicine infrastructure and ensure its accessibility and success in a postpandemic world.

Since the SARS-CoV-2 virus caused a global pandemic, the amount of misinformation in various media outlets has been on the rise. This has caused confusion among both healthcare workers and the general population about what the proper precautions against COVID-19 are. This study investigated attitudes towards misinformation related to protective measures that can be taken against COVID-19. The study was conducted in Saudi Arabia using an online survey questionnaire during the first three months of lockdown responding to the pandemic. The sample size of the study was N = 1294, of which 275 were healthcare workers and 974 were non-healthcare workers. The findings indicate that the Saudi Arabian population has a “Neutral” attitude towards COVID-19-related misinformation, meaning that, overall, they neither agree nor disagree with the most common COVID-19-related misinformation. Both healthcare workers and non-healthcare workers displayed a “Neutral” attitude towards herbal remedies for COVID-19. The level of agreement regarding the SARS-CoV-2 virus remaining in the throat for two days and the BCG vaccine protecting against COVID-19 was low. The findings of this research imply that knowledge dissemination is severely lacking in Saudi Arabia and that the Ministry of Health in Saudi Arabia should sincerely consider educating healthcare workers better about verified and true information regarding COVID-19. Conclusion: Future research should include larger sample sizes for each of the healthcare specialties surveyed in this study and analyse their attitudes towards COVID-19 misinformation.

Background Videos relating to suicide are available on YouTube, but their characteristics and impacts have seldom been examined. Objective This study aimed to examine YouTube videos posted in response to a sudden spate of student suicides in Hong Kong during the 2015-2016 school year and evaluate the impacts of those videos. Methods Keyword search was performed on YouTube, and relevant videos were identified. Video typology was examined through content analysis, specifically grouping the videos by who uploaded the videos, what presentation formats were used in the videos, whether the videos were originally created by the uploaders, and whether the videos disclosed the uploaders’ personal experiences with suicide. Impacts of the videos were assessed in terms of reach (measured by view count), engagement (measured by comment count), and insights (measured as to what extent the comments to each video could reveal personal suicide risk and attitude toward help-seeking). Statistical analysis was conducted to compare the impacts of different types of videos. The 7 most impactful videos that were originally created by the YouTubers were selected for further analysis. They were compared with 7 videos uploaded by the same YouTubers right before the student suicide videos and 7 right after the student suicide videos. The comparison focused on their impacts and the network structure of the comments to those videos. Results A total of 162 relevant YouTube videos were identified. They were uploaded by 7 types of stakeholders, and the most common format was one person talking to the camera. A total of 87.0% (141/162) of the videos were originally created by the uploaders and only 8.0% (13/162) of the videos disclosed uploader personal experiences with suicide. The uploader profiles being popular or top YouTubers and the video containing disclosure of the uploader’s personal experiences were found to be significantly correlated with greater impacts (P Conclusions When responding to a youth suicide crisis, videos made by key opinion leaders on YouTube sharing their own experiences of overcoming suicide risks could generate significant positive impacts. These types of videos offer a precious opportunity to craft online campaigns and activities to raise suicide prevention awareness and engage vulnerable youth.

Background Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. Objective We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. Methods Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. Results Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs 0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). Conclusions By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.