The interest in online museum experiences and the influence of uncertainty and sentiment factors on tourist arrivals: The case of EU Mediterranean countries

This study examines the determinants of tourist arrivals at hotels and short-stay accommodations for nine EU countries from January 2010 to March 2022. We identify four driving channels of foreign and domestic tourism flows: a traditional, a sentiment, a technological and a health channel. The latter comprises two novel variables: the museum search interest and the infectious disease equity market volatility tracker. The results reveal that traditional and new drivers related to market sentiments and interest in online tourism experiences affect arrivals. Notably, there is a substitution effect between online and in-presence tourism, and the larger the uncertainty, the more substantial the reduction in tourist arrivals. COVID-19 has affected especially Spain and Italy and more foreign than domestic tourists. © 2023 The Authors

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.

Introduction: Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods: The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results: Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion: Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure.

This paper investigates the relationship between investors' attention, as measured by Google search queries, and equity implied volatility during the COVID-19 outbreak. Recent studies show that search investors' behavior data is an extremely abundant repository of predictive data, and investor-limited attention increases when the uncertainty level is high. Our study using data from thirteen countries across the globe during the first wave of the COVID-19 pandemic (January-April 2020) examines whether the search "topic and terms" for the pandemic affect market participants' expectations about future realized volatility. With the panic and uncertainty about COVID-19, our empirical findings show that increased internet searches during the pandemic caused the information to flow into the financial markets at a faster rate and thus resulting in higher implied volatility directly and via the stock return-risk relation. More specifically for the latter, the leverage effect in the VIX becomes stronger as Google search queries intensify. Both the direct and indirect effects on implied volatility, highlight a risk-aversion channel that operates during the pandemic. We also find that these effects are stronger in Europe than in the rest of the world. Moreover, in a panel vector autoregression framework, we show that a positive shock on stock returns may soothe COVID-related Google searches in Europe. Our findings suggest that Google-based attention to COVID-19 leads to elevated risk aversion in stock markets.

Coronavirus-Related Searches on the Internet Predict COVID-19 Vaccination Rates in the Real World: A Behavioral Immune System Perspective.

According to the smoke detector and functional flexibility principles of human behavioral immune system (BIS), the exposure to COVID-19 cues could motivate vaccine uptake. Using the tool of Google Trends, we tested that coronavirus-related searches-which assessed natural exposure to COVID-19 cues-would positively predict actual vaccination rates. As expected, coronavirus-related searches positively and significantly predicted vaccination rates in the United States (Study 1a) and across the globe (Study 2a) after accounting for a range of covariates. The stationary time series analyses with covariates and autocorrelation structure of the dependent variable confirmed that more coronavirus-related searches compared with last week indicated increases in vaccination rates compared with last week in the United States (Study 1b) and across the globe (Study 2b). With real-time web search data, psychological scientists could test their research questions in real-life settings and at a large scale to expand the ecological validity and generalizability of the findings.

Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study.

BACKGROUND: Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, use for this purpose has been limited. OBJECTIVE: The purpose of this study is to determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. METHODS: Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords "chikungunya," "Ebola," "H1N1," "MERS," "SARS," and "Zika," 6 highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these 6 terms, unpaired 2-tailed t tests were used to compare the 2020 weekly scores for each term to their average level over the 3-year study period. The number of Reddit comments per month with each of these 6 terms was collected and then adjusted for the total estimated Reddit monthly comment volume to derive a measure of relative use, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term's pre-COVID monthly comment data, again using unpaired 2-tailed t tests. P value cutoffs for statistical significance were determined a priori with a Bonferroni correction. RESULTS: Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, peaking in March 2020. Google searches for "SARS" and "MERS" remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words "Ebola," "H1N1," "MERS," and "SARS" (P<.001, for each weekly or monthly comparison, respectively). CONCLUSIONS: Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health-related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for 6 distinct infectious causes of epidemics over the last 2 decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider using Google Trends and social media data to identify patterns of engagement with public health topics in real time and to optimize the timing of public health campaigns.

Pandemic- a candidate for the word of the century? The linguistic image of the lexeme in contemporary Polish based on corpus data

The article presents the linguistic image of the noun pandemic in contemporary Polish. The aim of the analysis is to look at how the word is used today, in the COVID-19 era, and how it was used before the first cases of this disease were detected. The first part of the article discusses the current data available on the Words of the Day website, Google Trends and the MoncoPL corpus search engine, while the second part shows the occurrences of the lexeme in slightly older texts that were collected in the National Corpus of Polish (NKJP) before 2010. The analysis of the material extracted from the above sources indicates that that the noun pandemic was very rare in the NKJP and then became very popular with the appearance of the pathogen causing COVID-19. However, the analysis of the most common collocates shows that the mentioned event did not have a significant impact on the other aspects of the use of the lexeme by the users of modern Polish.

Effect of the COVID-19 Pandemic on Global Interest in Plastic Surgery.

Introduction: Due to the SARS-CoV-2 (COVID-19) pandemic, many elective surgeries were canceled, including most aesthetic plastic surgery procedures. Although studies have shown COVID-19's effect on plastic surgery in the United States, no study to date has examined the international interest in plastic surgery procedures after the start of the COVID-19 pandemic. Thus, we sought to find this effect using the Google Trends tool. Material and Methods: The most common cosmetic procedures and top countries with the highest plastic surgery volume were selected from the International Society of Plastic Surgeons report and used as the search terms for Google Trends. Weekly search data from each procedure and country were collected from March 18, 2018 to March 13, 2022, split into 2 periods according to the start of the US COVID-19 lockdown, and compared. Results: Among the countries, the United States had the most plastic surgery interest after the COVID-19 pandemic, with India and Mexico closely following. On the other hand, Russia and Japan had the fewest changes in procedure interest. Regarding specific procedures, interest in breast augmentation, forehead lift, injectable filler, laser hair removal, liposuction, microdermabrasion, and rhytidectomy increased in all countries after the COVID-19 pandemic. Conclusions: After COVID-19, there has been increasing interest in almost all plastic surgery procedures globally, especially nonsurgical procedures and facial plastic surgery, with the greatest increases in the United States, India, and Mexico. These results can help inform plastic surgeons which procedures to focus on and which devices or technologies to invest in that are specific to their country.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Comparing health consumer search behavior and scientific research productivity related to COVID-19 vaccines in the USA: an infodemiology study

PurposeWhile vaccines are an effective preventative measure to defend against the spread and harmful symptoms of COVID-19, information about COVID vaccines can be difficult to find and conflicting in its coverage of vaccines' benefits and risks. This study aims to examine the extent to which Americans are searching for information about the three major vaccine producers (Pfizer-BioNTech, Moderna and Johnson & Johnson's Janssen) in relation to the amount of reliable scholarly information that has been produced about each one.Design/methodology/approachData were retrieved from Google Trends for the US Web users alongside scientific research output of the US scientists toward three Centers for Disease Control and Prevention (CDC)-authorized COVID-19 vaccines in Web of Science, Scopus and PubMed. The authors searched for descriptive statistical analyses to detect coronavirus-seeking behavior versus coronavirus releases in the USA from May 1, 2020, to April 30, 2021.FindingsOf the three COVID-19 vaccines, Pfizer has attracted more attention from the US population. However, the greatest number of articles about COVID-19 vaccines published by the US scholars belonged to Moderna (M = 8.17), with Pfizer (M = 7.75) having slightly less, and Janssen (M = 0.83) well behind. A positive association was found between COVID-19 vaccine information-seeking behavior (ISB) on Google and the amount of research produced about that vaccine (P <0.001).Research limitations/implicationsAs the researchers use the single search engine, Google, to retrieve data from the USA, thus, selection bias will be existing as Google only gathers the data of people who chose to get the information by using this search engine.Practical implicationsIf the policymakers in the US Department of Health and Human Services or the US CDC desire to improve the country's health ISB and the scientific publication behavior (SPB) of the US researchers regarding COVID-19 vaccines studies, they should reference the results of such a study.Originality/valueFrom an infodemiological viewpoint, these findings may support the health policymakers, as well as researchers who work on COVID-19 vaccines in the USA.

America's decoupling from China narrative: development, determinants, and policy implications

America's decoupling from China debate started after July 2018, reached its peak in August 2020, and is likely to continue even if it may not be a high priority for the Biden administration. Many studies have examined various aspects of this topic. Unlike previous research, using Google Trends data, this study creatively created a high-frequency weekly dataset to measure the narrative of decoupling from China in the US. Based on this dataset from January 2020 to June 2021, three issues are examined from a novel perspective. First, this study provides a quantitative description of its development. Second, for the first time in the academic literature, this study provides empirical evidence on the determinants of the decoupling narrative, including Chinese trade, Chinese investment, Chinese students, Chinese technology, Chinese companies, and Covid-19. Third, this study also discusses the policy implications of these findings. In particular, if the US government wants to adopt an aggressive strategy of decoupling from China in the future, COVID-19 is one tool that could be used. While this study makes original contributions to policy-makers, it also contributes to academia by presenting a (still) new quantitative approach to international relations. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Relationship Between Public Interest and Surgical Demand During the COVID-19 Pandemic.

INTRODUCTION: Surgical databases are useful for examining outcomes and case volume to improve care, while public interest data has the potential to track the supply and demand of medical services in specific communities. However, the relationship between public interest data and case volume from surgical databases, specifically during disruptive instances like the coronavirus pandemic, is unknown. Therefore, the purpose of this study is to determine how public interest data is related to the case volume of coronavirus and other surgical procedures performed during the coronavirus pandemic. METHODS: This retrospective study included a review of appendectomy, total hip arthroplasty (THA), and total knee arthroplasty (TKA) cases from the National Surgery Quality Improvement Project and relative search volume (RSV) of hip replacement, knee replacement, appendicitis, and coronavirus from Google Trends from 2019 to 2020. T-tests were used to compare surgical caseload and RSV data before and after the COVID-19 surge in March 2020, while linear models were used to determine relationships between confirmed procedures and relative search volumes. RESULTS: The RSV for knee replacement (p < 0.001, Cohen's D [d] = -5.01, 95% confidence interval [CI]: -7.64 to -2.34) and hip replacement (p < 0.001, d = -7.22, 95% CI: -10.85 to -3.57) had a large dip during the coronavirus pandemic, while the RSV for appendicitis had a smaller dip (p = 0.003, d = -2.37, 95% CI: -3.93 to -0.74). Linear models showed very strong linear relationships between surgical RSV and surgical volume for TKA (R2 = 0.931) and THA (R2 = 0.940). CONCLUSIONS: There was a significant reduction in the number of elective surgeries, which correlated to drops in public interest during COVID-19. The strong correlations between RSV, surgical volume, and coronavirus cases indicate that public interest can be used to track and predict surgical case volume. Our findings allow for greater insight into the use of public interest data to gauge surgical demand.

Impact of COVID-19 on Public Interest in Breast Cancer Screening and Related Symptoms: Google Trends Analysis.

BACKGROUND: The COVID-19 pandemic has led to a decrease in cancer screening due to the redeployment of health care resources and public avoidance of health care facilities. Breast cancer is the most common cancer diagnosed in female individuals, with improved survival rates from early detection. An avoidance of screening, resulting in late detection, greatly affects survival and increases health care resource burden and costs. OBJECTIVE: This study aimed to evaluate if a sustained decrease in public interest in screening occurred and to evaluate other search terms, and hence interest, associated with that. METHODS: This study used Google Trends to analyze public interest in breast cancer screening and symptoms. We queried search data for 4 keyword terms ("mammogram," "breast pain," "breast lump," and "nipple discharge") from January 1, 2019, to January 1, 2022. The relative search frequency metric was used to assess interest in these terms, and related queries were retrieved for each keyword to evaluate trends in search patterns. RESULTS: Despite an initial drastic drop in interest in mammography from March to April 2020, this quickly recovered by July 2020. After this period, alongside the recovery of interest in screening, there was a rapid increase in interest for arranging for mammography. Relative search frequencies of perceived breast cancer-related symptoms such as breast lump, nipple discharge, and breast pain remained stable. There was increase public interest in natural and alternative therapy of breast lumps despite the recovery of interest in mammography and breast biopsy. There was a significant correlation between search activity and Breast Cancer Awareness Month in October. CONCLUSIONS: Online search interest in breast cancer screening experienced a sharp decline at the beginning of the COVID-19 pandemic, with a subsequent return to baseline interest in arranging for mammography followed this short period of decreased interest.

Regenerative therapies for erectile dysfunction: the influence of direct-to-consumer marketing on patient interest.

Background: Despite a lack of evidence, a number of "regenerative" therapies have become popularized treatments for erectile dysfunction (ED). Platelet-rich plasma (PRP) injections and shockwave therapy have received significant attention through direct-to-consumer marketing and are advertised as viable alternatives to guideline-backed therapies. Additionally, focused low-intensity shock wave therapy (LiSWT) has become conflated with acoustic or radial wave therapy (rWT), although their mechanism of wave generation and tissue penetration is distinct. GAINSWave, a marketing platform for acoustic wave therapy, has also pervaded the marketplace. We aim to evaluate the relative impact of direct-to-consumer marketing of shockwave therapy and PRP by analyzing the quantity of Google internet search queries for selected regenerative and guideline-backed non-regenerative therapies for ED. Methods: National Google Search trends in the United States (www.google.com/trends) were analyzed to characterize interest in different forms of therapy for ED. Search trends for PRP, LiSWT (and various iterations), intracavernosal injections (ICI), intraurethral injections (IU), vacuum erectile device (VED), and GAINSWave were analyzed. Monthly search data were compiled over multiple years, ending at 2/28/2020, just before the COVID-19 pandemic and state of emergency in the United States. Macro-level changes in public interest were quantified using yearly averages. Results: Patterns in Google Search interest in PRP and LiSWT increased respectively by 3-fold and 275-fold over the decade, representing a larger share of Google Searches by 2020. Trends in Google Search interest in selected types of shockwave therapy for ED also show that queries for GAINSWave commanded public interest, increasing by 219-fold from 2016 to 2020. Conclusions: Regenerative therapies for ED have produced interest surpassing other adjunct guideline-backed therapies, despite receiving the designation of "experimental" or "investigational" therapies. The establishment of GAINSWave also constitutes an inflection point for the whole shockwave market: searches for shockwave therapy increased by 782% between 2016 and 2020. Direct-to-consumer marketing of PRP and shockwave therapy has upturned the customary role of physicians in counseling patients about evidence-based therapies for ED. This increase in public interest in GAINSWave emphasizes its success as a marketing platform. The urological community should consider strategies to address misinformation, such as search-engine optimization, social media, and educational outreach.

THE COVID-19 LOCKDOWN EFFECTS ON MENTAL WELL-BEING AND RELIGIOSITY: EVIDENCE FROM INDONESIA

We investigate the effects of COVID-19 lockdowns on frequency of online search on mental well-being and religiosity-related terms in Indonesia using high-frequency data from Google Trends and Bank Indonesia Consumer Survey from January 1st, 2018, to February 28th, 2021. Monthly search terms and consumer survey data are merged at the provincial level, which results in a total of 131,300 individual observations. Using event analysis and instrumental variable approaches, our study suggests that lockdown policy is significantly associated with higher search intensity of mental well-being and religiosity-related terms compared to the pre-lockdown period. Our findings suggest that mentally disturbed people tend to lean on religion to cope with stressful events during a crisis. Our study has substantial policy implications on ensuring appropriate government interventions that minimize the detrimental effect of COVID-19 on mental well-being. © 2023 University of Ljubljana - Veterinary Faculty. All rights reserved.

A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE

Analyzing airlines stock price volatility during COVID‐19 pandemic through internet search data

Recent Coronavirus pandemic has prompted many regulations which are affecting the stock market. Especially because of lockdown policies across the world, the airlines industry is suffering. We analyse the stock price movements of three major airlines companies using a new approach which leverages a measure of internet concern on different topics. In this approach, Twitter data and Google Trends are used to create a set of predictors which then leads to an appropriately modified GARCH model. In the analysis, first we show that the ongoing pandemic has an unprecedented severe effect. Then, the proposed model is used to analyse and forecast stock price volatility of the airlines companies. The findings establish that our approach can successfully use the effects of internet concern for different topics on the movement of stock price index and provide good forecasting accuracy. Model confidence set (MCS) procedure further shows that the short‐term volatility forecasts are more accurate for this method than other candidate models. Thus, it can be used to understand the stock market during a pandemic in a better way. Further, the proposed approach is attractive and flexible, and can be extended to other related problems as well.

GDP responses to supply chain disruptions in a post-pandemic era: Combination of DL and ANN outputs based on Google Trends

With the recent Russian-Ukraine conflict, the frequency and intensity of disruptive shocks on major supply chains have risen, causing increasing food and energy security concerns for regulators. That is, the combination of newly available sophisticated deep learning tools with real-time series data may represent a fruitful policy direction because machines can identify patterns without being pre-conditioned calibration thanks to experimental data training. This paper employs Deep Learning (DL) and Artificial Neural Network (ANN) algorithms and aimed predicts GDP responses to supply chain disruptions, energy prices, economic policy uncertainty, and google trend in the US. Sampled data from 2008 to 2022 are monthly wrangled and embed different recession episodes connected to the subprime crisis of 2008, the COVID-19 pandemic, the recent invasion of Ukraine by Russia, and the current economic recession in the US. Both DL and ANN outputs empirically (and unanimously) demonstrated how sensitive monthly GDP variations are to dynamic changes in supply chain performances. Findings identify the substantial role of google trends in delivering a consistent fit to predicted GDP values, which has implications While a comparative discussion over the larger forecasting performance of DL compared to ANN experiments is offered, implications for global policy, decision-makers and firm managers are finally provided. © 2023 Elsevier Inc.

Assessing Google Searches for Toothache during COVID-19 Lockdowns.

Background: Lockdowns due to the coronavirus disease 2019 (COVID-19) pandemic forced many dental offices to be closed. This study aims to investigate the association between COVID-19 imposed lockdowns and online searches for toothache using Google Trends (GT). Methods: We investigated GT online searches for the term "toothache" within the past 5 years. The time frame for data gathering was considered as the initiation and end dates of national/regional lockdowns in each country. We used 1-way analysis of variance to identify statistical differences in relative search volumes (RSVs) between 2020 and 2016-2019 for each country. Results: Overall, 16 countries were included in our analyses. Among all countries, Indonesia (n = 100), Jamaica (n = 56), Philippines (n = 56), Iran (n = 52), and Turkey (47) had the highest RSVs for toothache in the specified period. Compared with the previous 4 years, higher RSVs were seen in the world (as a whole) (2020 RSVs, 94.4; vs 2019 RSVs, 77.8 [ P < 0.001]) and 13 countries (81.3% of the included countries). Conclusion: Generally, searching for the term "toothache" showed an increase during the COVID-19 lockdowns in 2020 compared with the past 4 years. This can imply the importance of dental care as urgent medical care during public health emergencies such as COVID-19.

Online Health Information Seeking for Mpox in Endemic and Nonendemic Countries: Google Trends Study.

BACKGROUND: The recent global outbreak of mpox (monkeypox) has already been declared a public health emergency of international concern by the World Health Organization. Given the health, social, and economic impacts of the COVID-19 pandemic, there is understandable concern and anxiety around the emergence of another infectious disease-especially one about which little is known. OBJECTIVE: We used Google Trends to explore online health information seeking patterns for mpox in endemic and nonendemic countries and investigated the impact of the publication of the first in-country case on internet search volume. METHODS: Google Trends is a publicly accessible and free data source that aggregates worldwide Google search data. Google search data were used as a surrogate measure of online health information seeking for 178 days between February 18 and August 18, 2022. Searching data were downloaded across this time period for nonendemic countries with the highest case count (United States, Spain, Germany, United Kingdom, and France) and 5 endemic countries (Democratic Republic of Congo, Nigeria, Ghana, Central African Republic, and Cameroon). Joinpoint regression analysis was used to measure changes in searching trends for mpox preceding and following the announcement of the first human case. RESULTS: Online health information seeking significantly increased after the publication of the first case in all the nonendemic countries-United States, Spain, Germany, United Kingdom, and France, as illustrated by significant joinpoint regression models. Joinpoint analysis revealed that models with 3 significant joinpoints were the most appropriate fit for these data, where the first joinpoint represents the initial rise in mpox searching trend, the second joinpoint reflects the start of the decrease in the mpox searching trend, and the third joinpoint represents searching trends' return to searching levels prior to the first case announcement. Although this model was also found in 2 endemic countries (ie, Ghana and Nigeria), it was not found in Central African Republic, Democratic Republic of Congo, or Cameroon. CONCLUSIONS: Findings demonstrate a surge in online heath information seeking relating to mpox after the first in-country case was publicized in all the nonendemic countries and in Ghana and Nigeria among the endemic counties. The observed increases in mpox searching levels are characterized by sharp but short-lived periods of searching before steep declines back to levels observed prior to the publication of the first case. These findings emphasize the importance of the provision of accurate, relevant online public health information during disease outbreaks. However, online health information seeking behaviors only occur for a short time period, and the provision of accurate information needs to be timely in relation to the publication of new case-related information.