Correlations of Online Search Engine Trends With Coronavirus Disease (COVID-19) Incidence: Infodemiology Study

Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks

Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilising big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. The present review gives an overview of digital epidemiology, emphasising its importance in the timely detection of infectious disease outbreaks. Researchers may discover and track outbreaks in real time using digital data sources such as search engine queries, social media trends, and digital health records. However, data quality, concerns about privacy, and data interoperability must be addressed to maximise the effectiveness of digital epidemiology. As the global landscape of infectious diseases evolves, integrating digital epidemiology becomes critical to improving pandemic preparedness and response efforts. Integrating digital epidemiology into routine monitoring systems has the potential to improve global health outcomes and save lives in the event of viral outbreaks.

Burden of atopic dermatitis in Europe: A population-centred approach leveraging web search data in 21 European countries

BACKGROUND

The complexity, high prevalence, and substantial personal and socioeconomic burden collectively render atopic dermatitis (AD) a major public health concern. Using crowdsourced Internet data has the potential to provide unique insights into this concern, as demonstrated by several previous studies. However, a comprehensive comparison across European countries remains lacking.

OBJECTIVES

The study aimed to investigate AD-related web searches across Europe to assess spatiotemporal variations and associations between disease-related and external factors.

METHODS

AD-related web search data were extracted for 21 European countries between February 2019 and January 2023. Descriptive analysis and autocorrelation functions were performed to examine spatiotemporal patterns. Correlations (r) were used to evaluate the associations between web searches and disease-related, socioeconomic and meteorological data.

RESULTS

Over 241 million AD-related web searches were identified, with search volume varying substantially among European countries (p < 0.001) and correlating with AD prevalence and disease burden (both r = 0.51, p = 0.019). Search volume increased between 2019 and 2023 in all countries and seasonally peaked in January and March. Negative correlations with median population age (r = -0.46, p = 0.039), number of general practitioners (r = -0.29, p = 0.226) and specialists (r = -0.27, p = 0.270) were observed. Moderate to strong correlations were found between search volume and cold, humid and windy weather with fewer sunshine hours, while higher online interest typically occurred 1-3 months after such weather conditions.

CONCLUSION

The study highlights the great potential of online crowdsourced data analysis, for example, to investigate the impact of climate change or to identify unmet needs at a population level. Furthermore, the growing online interest in AD and the corresponding seasonal peaks emphasize the necessity of adapting treatment plans, intensifying public health campaigns, and disseminating reliable online information by governments and healthcare providers, especially during these periods.

MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework, MGLEP, that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators.

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

The value of publicly engaged podcasting in human biology

Podcasting about science may be popular, but does it motivate engagement? The Human Biology Association and American Journal of Human Biology (AJHB) cosponsor a podcast that highlights recent articles. We hypothesized that AJHB articles discussed on the podcast (n = 42) receive more online views than other articles from the same issues. We used Student's t-tests to compare mean article views and found podcasted articles received more views, but the difference was not significant. When extreme outliers were removed, podcasted articles had over twice the views of control articles (p = .04), indicating that journal-affiliated podcasting can increase science engagement.

Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking

BACKGROUND

Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations.

METHOD

This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10-90 and 25-75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants.

RESULT

The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance.

CONCLUSION

Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Web-Based Search Volume for HIV Tests and HIV-Testing Preferences During the COVID-19 Pandemic in Japan: Infodemiology Study

BACKGROUND

Research has found a COVID-19 pandemic-related impact on HIV medical services, including clinic visits, testing, and antiviral therapy initiation in countries including Japan. However, the change in trend for HIV/AIDS testing during the COVID-19 pandemic has not been explored extensively in the Japanese population.

OBJECTIVE

This infodemiology study examines the web-based search interest for two types of HIV tests, self-test kits and facility-based tests, before and during the COVID-19 pandemic in Japan.

METHODS

The monthly search volume of queried search terms was obtained from Yahoo! JAPAN. Search volumes for the following terms were collected from November 2017 to October 2018: "HIV test," "HIV test kit," and "HIV test health center." The search term "Corona PCR" and the number of new COVID-19 cases by month were used as a control for the search trends. The number of new HIV cases in the corresponding study period was obtained from the AIDS Trend Committee Quarterly Report from the AIDS Prevention Foundation.

RESULTS

Compared to the search volume of "corona-PCR," which roughly fluctuated corresponding to the number of new COVID-19 cases in Japan, the search volume of "HIV test" was relatively stable from 2019 to 2022. When we further stratified by the type of HIV test, the respective web-based search interest in HIV self-testing and facility-based testing showed distinct patterns from 2018 to 2022. While the search volume of "HIV test kit" remained stable, that of "HIV test health center" displayed a decreasing trend starting in 2018 and has remained low since the beginning of the COVID-19 pandemic. Around 66%-71% of the search volume of "HIV test kits" was attributable to searches made by male internet users from 2018 to 2022, and the top three contributing age groups were those aged 30-39 (27%-32%), 20-29 (19%-32%), and 40-49 (19%-25%) years. On the other hand, the search volume of "HIV test health centers" by male users decreased from more than 500 from 2018 to 2019 to fewer than 300 from 2020 to 2022.

CONCLUSIONS

Our study found a notable decrease in the search volume of "HIV test health center" during the pandemic, while the search volume for HIV self-testing kits remained stable before and during the COVID-19 crisis in Japan. This suggests that the previously reported COVID-19-related decrease in the number of HIV tests mostly likely referred to facility-based testing. This sheds light on the change in HIV-testing preferences in Japan, calling for a more comprehensive application and regulatory acceptance of HIV self-instructed tests.

Online search interest in long-term symptoms of coronavirus disease 2019 during the COVID-19 pandemic in Japan: Infodemiology study using the most visited search engine in Japan

Prolonged symptoms that occur after COVID-19 (long-COVID) vary from mild, which do not interfere with daily life, to severe, which require long-term social support. This study assessed the secular trend in online searches on long-COVID in Japan. We conducted an observational study using data provided by Yahoo! JAPAN on the monthly search volume of query terms related to long-COVID from January 2020 to December 2022, including the search volume of the query "コロナ" (long-COVID in Japanese). The number of new cases of COVID-19 by month was used as a control for search trends, and the symptoms retrieved in conjunction with long-COVID were compared. Trends in online searches for each symptom of long-COVID were analyzed. The symptoms of long-COVID were classified according to "Component 1-Symptoms and Complaints" of the International Classification of Primary Care, 2nd edition (ICPC-2). Interest in long-COVID increased in response to peaks in the number of new cases of COVID-19 in Japan. The most frequent symptom searches with long-COVID were hair loss/baldness (3,530, 21,400, and 33,600 searches in 2020, 2021, and 2022, respectively), cough (340, 7,900 and 138,910 searches in 2020, 2021, and 2022, respectively), disturbance of smell/taste (230, 13,340, and 44,160 searches in 2020, 2021, and 2022, respectively), and headache (580, 6,180, and 42,870 searches in 2020, 2021, and 2022, respectively). In addition, the ranking of interest in "weakness/tiredness, general" in long-COVID increased each year (not in the top 10 in 2020, seventh in 2021, and second in 2022), and the absolute number of searches also increased. To our knowledge, this is the first study to investigate secular trends in online interest in long-COVID in the world. Continued monitoring of online interest in long-COVID is necessary to prepare for a possible increase in the number of patients with long-COVID.

Population Interest in Information on Obesity, Nutrition, and Occupational Health and Its Relationship with the Prevalence of Obesity: An Infodemiological Study

OBJECTIVE

To identify and analyze population interest in obesity, nutrition, and occupational health and safety and its relationship with the worldwide prevalence of obesity through information search trends.

METHOD

In this ecological study, data were obtained through online access to Google Trends using the topics "obesity", "nutrition", and "occupational health and safety". Obesity data were obtained from the World Health Organization (WHO) website for crude adult prevalence and estimates by region. The variables studied were relative search volume (RSV), temporal evolution, milestone, trend, and seasonality. The temporal evolution of the search trends was examined by regression analysis (R2). To assess the relationship between quantitative variables, the Spearman correlation coefficient (Rho) was used. Seasonality was verified using the augmented Dickey-Fuller (ADF) test.

RESULTS

The RSV trends were as follows: obesity (R2 = 0.04, p = 0.004); nutrition (R2 = 0.42, p < 0.001); and occupational health and safety (R2 = 0.45, p < 0.001). The analysis of seasonality showed the absence of a temporal pattern (p < 0.05 for all terms). The associations between world obesity prevalence (WOP) and the different RSVs were as follows: WOP versus RSV obesity, Rho = -0.79, p = 0.003; WOP versus RSV nutrition, Rho = 0.57, p = 0.044; and WOP versus RSV occupational health and safety, Rho = -0.93, p = 0.001.

CONCLUSIONS

Population interest in obesity continues to be a trend in countries with the highest prevalence, although there are clear signs popularity loss in favor of searches focused on possible solutions and treatments, with a notable increase in searches related to nutrition and diet. Despite the fact that most people spend a large part of their time in the workplace and that interventions including various strategies have been shown to be useful in combating overweight and obesity, there has been a decrease in the population's interest in information related to obesity in the workplace. This information can be used as a guide for public health approaches to obesity and its relationship to nutrition and a healthy diet, approaches that are of equal utility and applicability in occupational health.

Google Trends as a predictive tool in the era of COVID-19: a scoping review

Google Trends has been extensively used in different sectors from finance to tourism, the economy, fashion, the fun industry, the oil trade, and healthcare. This scoping review aims to summarize the role of Google Trends as a monitoring and a predicting tool in the COVID-19 pandemic. Inclusion criteria for this scoping review were original English-language peer-reviewed research articles on the COVID-19 pandemic conducted in 2020 using Google Trends as a search tool. Articles that were in a language other than English, were only in abstract form, or did not discuss the role of Google Trends during the COVID-19 pandemic were excluded. According to these criteria, a total of 81 studies were included to cover the period of the first year after the emergence of the crisis. Google Trends can potentially help health authorities to plan and control pandemics earlier and to decrease the risk of infection among people.

Vector Autoregression for Forecasting the Number of COVID-19 Cases and Analyzing Behavioral Indicators in the Philippines: Ecologic Time-Trend Study

BACKGROUND

Traditional surveillance systems rely on routine collection of data. The inherent delay in retrieval and analysis of data leads to reactionary rather than preventive measures. Forecasting and analysis of behavior-related data can supplement the information from traditional surveillance systems.

OBJECTIVE

We assessed the use of behavioral indicators, such as the general public's interest in the risk of contracting SARS-CoV-2 and changes in their mobility, in building a vector autoregression model for forecasting and analysis of the relationships of these indicators with the number of COVID-19 cases in the National Capital Region.

METHODS

An etiologic, time-trend, ecologic study design was used to forecast the daily number of cases in 3 periods during the resurgence of COVID-19. We determined the lag length by combining knowledge on the epidemiology of SARS-CoV-2 and information criteria measures. We fitted 2 models to the training data set and computed their out-of-sample forecasts. Model 1 contains changes in mobility and number of cases with a dummy variable for the day of the week, while model 2 also includes the general public's interest. The forecast accuracy of the models was compared using mean absolute percentage error. Granger causality test was performed to determine whether changes in mobility and public's interest improved the prediction of cases. We tested the assumptions of the model through the Augmented Dickey-Fuller test, Lagrange multiplier test, and assessment of the moduli of eigenvalues.

RESULTS

A vector autoregression (8) model was fitted to the training data as the information criteria measures suggest the appropriateness of 8. Both models generated forecasts with similar trends to the actual number of cases during the forecast period of August 11-18 and September 15-22. However, the difference in the performance of the 2 models became substantial from January 28 to February 4, as the accuracy of model 2 remained within reasonable limits (mean absolute percentage error [MAPE]=21.4%) while model 1 became inaccurate (MAPE=74.2%). The results of the Granger causality test suggest that the relationship of public interest with number of cases changed over time. During the forecast period of August 11-18, only change in mobility (P=.002) improved the forecasting of cases, while public interest was also found to Granger-cause the number of cases during September 15-22 (P=.001) and January 28 to February 4 (P=.003).

CONCLUSIONS

To the best of our knowledge, this is the first study that forecasted the number of COVID-19 cases and explored the relationship of behavioral indicators with the number of COVID-19 cases in the Philippines. The resemblance of the forecasts from model 2 with the actual data suggests its potential in providing information about future contingencies. Granger causality also implies the importance of examining changes in mobility and public interest for surveillance purposes.

The Effects of Online Health Information-Seeking Behavior on Sexually Transmitted Disease in China: Infodemiology Study of the Internet Search Queries

BACKGROUND

Sexually transmitted diseases (STDs) are a serious issue worldwide. With the popularity of the internet, online health information-seeking behavior (OHISB) has been widely adopted to improve health and prevent disease.

OBJECTIVE

This study aimed to investigate the short-term and long-term effects of different types of OHISBs on STDs, including syphilis, gonorrhea, and AIDS due to HIV, based on the Baidu index.

METHODS

Multisource big data were collected, including case numbers of STDs, search queries based on the Baidu index, provincial total population, male-female ratio, the proportion of the population older than 65 years, gross regional domestic product (GRDP), and health institution number data in 2011-2018 in mainland China. We categorized OHISBs into 4 types: concept, symptoms, treatment, and prevention. Before and after controlling for socioeconomic and medical conditions, we applied multiple linear regression to analyze associations between the Baidu search index (BSI) and Baidu search rate (BSR) and STD case numbers. In addition, we compared the effects of 4 types of OHISBs and performed time lag cross-correlation analyses to investigate the long-term effect of OHISB.

RESULTS

The distributions of both STD case numbers and OHISBs presented variability. For case number, syphilis, and gonorrhea, cases were mainly distributed in southeastern and northwestern areas of China, while HIV/AIDS cases were mostly distributed in southwestern areas. For the search query, the eastern region had the highest BSI and BSR, while the western region had the lowest ones. For 4 types of OHISB for 3 diseases, the BSI was positively related to the case number, while the BSR was significantly negatively related to the case number (P<.05). Different categories of OHISB have different effects on STD case numbers. Searches for prevention tended to have a larger impact, while searches for treatment tended to have a smaller impact. Besides, due to the time lag effect, those impacts would increase over time.

CONCLUSIONS

Our study validated the significant associations between 4 types of OHISBs and STD case numbers, and the impact of OHISBs on STDs became stronger over time. It may provide insights into how to use internet big data to better achieve disease surveillance and prevention goals.

Impact of COVID-19 on search in an organisation

COVID-19 has created unprecedented organisational challenges, yet no study has examined the impact on information search. A case study in a knowledge-intensive organisation was undertaken on 2.5 million search queries during the pandemic. A surge of unique users and COVID-19 search queries in March 2020 may equate to 'peak uncertainty and activity', demonstrating the importance of corporate search engines in times of crisis. Search volumes dropped 24% after lockdowns; an 'L-shaped' recovery may be a surrogate for business activity. COVID-19 search queries transitioned from awareness, to impact, strategy, response and ways of working that may influence future search design. Low click through rates imply some information needs were not met and searches on mental health increased. In extreme situations (i.e. a pandemic), companies may need to move faster, monitoring and exploiting their enterprise search logs in real time as these reflect uncertainty and anxiety that may exist in the enterprise.

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data

BACKGROUND

In the United States, 1 out of every 3 people lives in a mental health professional shortage area. Shortage areas tend to be rural, have higher levels of poverty, and have poor mental health outcomes. Previous work has demonstrated that these poor outcomes may arise from interactions between a lack of resources and lack of recognition of mental illness by medical professionals.

OBJECTIVE

We aimed to understand the differences in how people in shortage and nonshortage areas search for information about mental health on the web.

METHODS

We analyzed search engine log data related to health from 2017-2021 and examined the differences in mental health search behavior between shortage and nonshortage areas. We analyzed several axes of difference, including shortage versus nonshortage comparisons, urban versus rural comparisons, and temporal comparisons.

RESULTS

We found specific differences in search behavior between shortage and nonshortage areas. In shortage areas, broader and more general mental health symptom categories, namely anxiety (mean 2.03%, SD 0.44%), depression (mean 1.15%, SD 0.27%), fatigue (mean 1.21%, SD 0.28%), and headache (mean 1.03%, SD 0.23%), were searched significantly more often (Q<.0003). In contrast, specific symptom categories and mental health disorders such as binge eating (mean 0.02%, SD 0.02%), psychosis (mean 0.37%, SD 0.06%), and attention-deficit/hyperactivity disorder (mean 0.77%, SD 0.10%) were searched significantly more often (Q<.0009) in nonshortage areas. Although suicide rates are consistently known to be higher in shortage and rural areas, we see that the rates of suicide-related searching are lower in shortage areas (mean 0.05%, SD 0.04%) than in nonshortage areas (mean 0.10%, SD 0.03%; Q<.0003), more so when a shortage area is rural (mean 0.024%, SD 0.029%; Q<2 × 10^-12).

CONCLUSIONS

This study demonstrates differences in how people from geographically marginalized groups search on the web for mental health. One main implication of this work is the influence that search engine ranking algorithms and interface design might have on the kinds of resources that individuals use when in distress. Our results support the idea that search engine algorithm designers should be conscientious of the role that structural factors play in expressions of distress and they should attempt to design search engine algorithms and interfaces to close gaps in care.

Did the COVID-19 pandemic impact urticaria information-seeking behavior in China? A retrospective longitudinal study

Purpose

To investigate information-seeking behavior related to urticaria before and during the COVID-19 pandemic in China.

Methods

Search query data for terms related to urticaria were retrieved using Baidu Index database from October 23, 2017 to April 23, 2022, and daily COVID-19 vaccination doses data were obtained from the website of the Chinese Center for Disease Control and Prevention. Among the 23 eligible urticaria search terms, four urticaria themes were generated as classification, symptom, etiology, and treatment of urticarial, respectively. Baidu Search Index (BSI) value for each term were extracted to analyze and compare the spatial and temporal distribution of online search behavior for urticaria before and after the COVID-19 pandemic, and to also explore the correlation between search query and daily COVID-19 vaccination doses.

Results

The classification of urticaria accounted for nearly half of the urticaria queries on the internet. Regular seasonal patterns of BSI were observed in urticaria-related online search, by attaining its highest level in spring and summer and lowest level in winter. The BSIs of all urticaria themes significantly increased after the COVID-19 pandemic than that before the pandemic (all P<0.05). Xizang, Qinghai and Ningxia are the most active geographical areas for increased urticaria-searching activities after the COVID-19 pandemic. There was also a significant positive correlation between daily BSIs and daily COVID-19 vaccination doses in each urticaria theme. Cross-correlation analysis found that the search of symptom, etiology, and treatment attained their strongest correlation with daily COVID-19 vaccination doses at 11-27 days before the injection of vaccine, imply vaccination hesitation related to concerns of urticaria.

Conclusions

This study used the internet as a proxy to provide evidence of public search interest and spatiotemporal characteristics of urticaria, and revealed that the search behavior of urticaria have increased significantly after the COVID-19 pandemic and COVID-19 vaccination. It is anticipated that the findings about such increase in search behavior, as well as the behavior of urticaria-related vaccine-hesitancy, will help guide public health education and policy regulation.

Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

Revealing the spatiotemporal characteristics of the general public's panic levels during the pandemic crisis in China

The existing crisis management research mostly reveals the patterns of the public's panic levels from the perspectives of public management, sociology, and psychology, only a few studies have revealed the spatiotemporal characteristics. Therefore, this study investigates the spatial distribution and temporal patterns and influencing factors on the general public's panic levels using the Baidu Index data from a geographic perspective. The results show that: (1) The public's panic levels were significantly correlated with the spatial distance between the epicenter and the region of investigation, and with the number of confirmed cases in different regions when the pandemic began to spread. (2) Based on the spatial distance between the epicenter and the region, the public's panic levels in different regions could be divided into three segments: core segment (0-500 km), buffer segment (500-1300 km), and peripheral segment (>1300 km). The panic levels of different people in the three segments were consistent with the Psychological Typhoon Eye Effect and the Ripple Effect can be detected in the buffer segment. (3) The public's panic levels were strongly correlated with whether the spread of the infectious disease crisis occurred and how long it lasted. It is suggested that crisis information management in the future needs to pay more attention to the spatial division of control measures. The type of crisis information released to the general public should depend on the spatial relationship associated with the place where the crisis breaks out.

Outpatient purchasing patterns of hydroxychloroquine and ivermectin in the USA and Canada during the COVID-19 pandemic: an interrupted time series analysis from 2016 to 2021

BACKGROUND

Hydroxychloroquine and ivermectin received widespread attention after initial studies suggested that they were effective against COVID-19. However, several of these studies were later discredited.

OBJECTIVES

We explored the impact of scientific articles, public announcements and social media posts on hydroxychloroquine and ivermectin purchases in the USA and Canada during the COVID-19 pandemic.

METHODS

We conducted a retrospective, population-based time series analysis of retail hydroxychloroquine and ivermectin purchases in the USA and Canada from February 2016 through to December 2021, using IQVIA's Multinational Integrated Data Analysis database. We fitted the purchasing rates with interventional autoregressive integrated moving average models. We used Google Trends to identify the most influential interventions to include in the models.

RESULTS

There were significant pulse increases in hydroxychloroquine purchases in March 2020 in both the USA (P < 0.0001) and Canada (P < 0.0001). For ivermectin, there were no significant changes in April 2020 in either the USA (P = 0.41) or Canada (P = 0.16); however, significant pulse increases occurred from December 2020 to January 2021 in both the USA (P = 0.0006) and Canada (P < 0.0001), as well as significant ramp increases from April to August 2021 in both the USA (P < 0.0001) and Canada (P = 0.02). The increases in ivermectin purchases were larger in the USA than in Canada.

CONCLUSIONS

Increases in hydroxychloroquine and ivermectin purchasing rates aligned with controversial scientific articles and social media posts. This highlights the importance of scientific integrity and disseminating accurate epidemiologic information during pandemics.

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss

Aim

To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss.

Methods

Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords "loss of smell" and "loss of taste" were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases.

Results

From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19.

Conclusion

Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1-3 weeks in advance.

Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study

Background

Coronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends.

Methods

HFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic “HFMD” from 2009 to 2021. Japanese tweets containing “HFMD” were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation.

Results

HFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic “HFMD” presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic.

Conclusions

The significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07790-9.

Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review

The probability of future Coronavirus Disease (COVID)-19 waves remains high, thus COVID-19 surveillance and forecasting remains important. Online search engines harvest vast amounts of data from the general population in real time and make these data publicly accessible via such tools as Google Trends (GT). Therefore, the aim of this study was to review the literature about possible use of GT for COVID-19 surveillance and prediction of its outbreaks. We collected and reviewed articles about the possible use of GT for COVID-19 surveillance published in the first 2 years of the pandemic. We resulted in 54 publications that were used in this review. The majority of the studies (83.3%) included in this review showed positive results of the possible use of GT for forecasting COVID-19 outbreaks. Most of the studies were performed in English-speaking countries (61.1%). The most frequently used keyword was "coronavirus" (53.7%), followed by "COVID-19" (31.5%) and "COVID" (20.4%). Many authors have made analyses in multiple countries (46.3%) and obtained the same results for the majority of them, thus showing the robustness of the chosen methods. Various methods including long short-term memory (3.7%), random forest regression (3.7%), Adaboost algorithm (1.9%), autoregressive integrated moving average, neural network autoregression (1.9%), and vector error correction modeling (1.9%) were used for the analysis. It was seen that most of the publications with positive results (72.2%) were using data from the first wave of the COVID-19 pandemic. Later, the search volumes reduced even though the incidence peaked. In most countries, the use of GT data showed to be beneficial for forecasting and surveillance of COVID-19 spread.

Assessing the online search behavior for COVID-19 outbreak: Evidence from Iran

Introduction

Google Trends (GT) is an important free tool for online search behavior analysis, which provides access to Internet search patterns in Google. In recent decades, this database has been used for predicting the outbreak of epidemics and pandemics in different regions of the world. The present study aimed to evaluate Iranian users’ COVID-19-related online search behavior.

Methods

This longitudinal study was conducted in 2021. The data of Iranian users’ COVID-19-related online search behavior (trend) were collected from the GT website, and the epidemiological data of the COVID-19 outbreak in Iran from 16 February 2020 to 2 January 2021 were sourced from the Iranian ministry of health and medical education, as well as the World Health Organization. The data were analyzed in SPSS using descriptive and inferential statistics.

Results

All the COVID-19-related search terms in Iran gained their highest popularity value (relative search volume = 100) in the first 8 weeks of the pandemic, and then this value assumed a decreasing trend over time. Based on factor analysis, relative search volume (RSV) of factor 1 terms (related to corona [in Persian] and corona) have a low significance relationship with COVID-19 epidemiological data in one-, two-, and three-week time lags. Although, RSV of factor 2 terms (related to COVID [in Persian], COVID-19, and coronavirus) correlated with the total weekly number of COVID-19 cases in mentioned time lags.

Conclusion

COVID-19-related search terms were popular among Iranian users at the beginning of the pandemic. The online search queries and the key terms searched by Iranian users varied during the COVID-19 pandemic. This study provides evidence in favor of the adoption of GT as an epidemiological surveillance tool but, it is necessary to consider that mass media and other confounders can significantly influence RSVs.

Language Barriers to Online Search Interest for COVID-19: A Global Infodemiological Study

Background

Implementation of coronavirus disease 2019 (COVID-19) pandemic control measures requires the engagement and participation of the public in a synchronized manner. Language may be a barrier to captivating public interest in a concerted manner. The relative volume of English and non-English COVID-19-related web searches estimate public interest among English and Non-English “searchers,” respectively. Asynchrony between English and non-English search interest may suggest language-related lapses in public engagement. Addressing these lapses may improve public health communications. In this study, we aimed to describe the distribution and temporal trends in the evolution of English and non-English online search interest for COVID-19 and to identify lags between English and non-English search interest.

Methodology

Search interest data (Baidu Index for China, Google Trends for other countries) was queried for the keywords “coronavirus,” “covid 19,” and their non-English equivalents between January 1, 2019, and September 30, 2020, for each country (n = 230). Daily total, English, and non-English search interest were recorded. Search Interest variables were described at global, regional, and country levels. The cross-correlation function was used to identify lags between English and non-English search interest at global, regional, and country levels.

Results

Globally, 9.69% of total searches relating to COVID-19 utilized non-English keywords. Among included regions, 64.7% (11/17) had significant non-English interest. Central Asia had the highest proportion of non-English interest (81.13% of total interest), followed by Eastern Europe (56.17%), Eastern Asia, Western Asia, and Northern Africa (all over 20%). Among included countries, 33.5% (77/230) had significant non-English interest. Cross-correlation function identified significant lags between English and non-English Interest in six regions (median lag [interquartile range, IQR]: -0.5 [6.00] days) and 24 countries (median lag [IQR]: -1 [4.25] days).

Conclusions

Non-English keywords contribute substantially to searches relating to COVID-19 in certain countries and regions. Numerous locations exhibit significant lags between English and non-English search interest, suggesting language-related discrepancies in the interest for COVID-19. Further research is required to address the root cause of these lags.

Digital health literacy, online information-seeking behaviour, and satisfaction of Covid-19 information among the university students of East and South-East Asia

During the COVID-19 pandemic, there is a growing interest in online information about coronavirus worldwide. This study aimed to investigate the digital health literacy (DHL) level, information-seeking behaviour, and satisfaction of information on COVID-19 among East and South-East Asia university students. This cross-sectional web-based study was conducted between April to June 2020 by recruiting students from universities in China, Malaysia, and the Philippines. University students who have Internet access were invited to participate in the study. Items on sociodemographic variables, DHL, information-seeking behaviour, and information satisfaction were included in the questionnaire. Descriptive statistics and logistic regression analysis were conducted. A total of 5302 university students responded to the survey. The overall mean score across the four DHL subscales was 2.89 (SD: 0.42). Search engines (e.g., Google, Bing, Yahoo) (92.0%) and social media (88.4%) were highly utilized by the students, whereas Websites of doctors or health insurance companies were of lower utilization (64.7%). Across the domains (i.e., adding self-generated content, determining relevance, evaluating reliability, and protecting privacy) higher DHL was positively associated with higher usage of trustworthy resources. Providing online information on COVID-19 at official university websites and conducting health talks or web-based information dissemination about the strategies for mental health challenges during pandemic could be beneficial to the students. Strengthening DHL among university students will enhance their critical thinking and evaluation of online resources, which could direct them to the quality and trustworthy information sources on COVID-19.

Using ‘infodemics’ to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July—September 2020

Background

Trends in the public perception and awareness of COVID-19 over time are poorly understood. We conducted a longitudinal study to analyze characteristics and trends of online information during a major COVID-19 outbreak in Da Nang province, Vietnam in July-August 2020 to understand public awareness and perceptions during an epidemic.

Methods

We collected online information on COVID-19 incidence and mortality from online platforms in Vietnam between 1 July and 15 September, 2020, and assessed their trends over time against the epidemic curve. We explored the associations between engagement, sentiment polarity, and other characteristics of online information with different outbreak phases using Poisson regression and multinomial logistic regression analysis. We assessed the frequency of keywords over time, and conducted a semantic analysis of keywords using word segmentation.

Results

We found a close association between collected online information and the evolution of the COVID-19 situation in Vietnam. Online information generated higher engagements during compared to before the outbreak. There was a close relationship between sentiment polarity and posts’ topics: the emotional tendencies about COVID-19 mortality were significantly more negative, and more neutral or positive about COVID-19 incidence. Online newspaper reported significantly more information in negative or positive sentiment than online forums or social media. Most topics of public concern followed closely the progression of the COVID-19 situation during the outbreak: development of the global pandemic and vaccination; the unfolding outbreak in Vietnam; and the subsiding of the outbreak after two months.

Conclusion

This study shows how online information can reflect a public health threat in real time, and provides important insights about public awareness and perception during different outbreak phases. Our findings can help public health decision makers in Vietnam and other low and middle income countries with high internet penetration rates to design more effective communication strategies during critical phases of an epidemic.

Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study

Background

The search for health information from web-based resources raises opportunities to inform the service operations of health care systems. Google Trends search query data have been used to study public health topics, such as seasonal influenza, suicide, and prescription drug abuse; however, there is a paucity of literature using Google Trends data to improve emergency department patient-volume forecasting.

Objective

We assessed the ability of Google Trends search query data to improve the performance of adult emergency department daily volume prediction models.

Methods

Google Trends search query data related to chief complaints and health care facilities were collected from Chicago, Illinois (July 2015 to June 2017). We calculated correlations between Google Trends search query data and emergency department daily patient volumes from a tertiary care adult hospital in Chicago. A baseline multiple linear regression model of emergency department daily volume with traditional predictors was augmented with Google Trends search query data; model performance was measured using mean absolute error and mean absolute percentage error.

Results

There were substantial correlations between emergency department daily volume and Google Trends "hospital" (r=0.54), combined terms (r=0.50), and "Northwestern Memorial Hospital" (r=0.34) search query data. The final Google Trends data-augmented model included the predictors Combined 3-day moving average and Hospital 3-day moving average and performed better (mean absolute percentage error 6.42%) than the final baseline model (mean absolute percentage error 6.67%)-an improvement of 3.1%.

Conclusions

The incorporation of Google Trends search query data into an adult tertiary care hospital emergency department daily volume prediction model modestly improved model performance. Further development of advanced models with comprehensive search query terms and complementary data sources may improve prediction performance and could be an avenue for further research.

Anosmia-related internet search and the course of the first wave of the COVID-19 pandemic in the United States

Background

The current pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China. Although the first case in the United States was reported on Jan 20, 2020 in Washington, the early pandemic time course is uncertain. One approach with the potential to provide more insight into this time course is the examination of search activity. This study analyzed US search data prior to the first press release of anosmia as an early symptom (March 20, 2020).

Methods

Daily internet search query data was obtained from Google Trends (September 20th to March 20th for 2015 to 2020) both for the United States and on a state-by-state basis. Normalized anosmia-related search activity for the years prior to the pandemic was averaged to obtain a baseline level. Cross-correlations were performed to determine the time-lag between changes in search activity and SARS-CoV-2 cases/deaths.

Results

Only New York showed both significant increases in anosmia-related terms during the pandemic year as well as a significant lag (6 days) between increases in search activity and the number of cases/deaths attributed to SARS-CoV-2.

Conclusions

There is no evidence from search activity to suggest earlier spread of SARS-CoV-2 than has been previously reported. The increase in anosmia-related searches preceded increases in SARS-CoV-2 cases/deaths by 6 days, but this was only significant over the background noise of searches for other reasons in the setting of a very large outbreak (New York in the spring of 2020).

•

Recent work has suggested using digital epidemiology to follow pandemics.

•

In our view, these previous studies have several methodological errors.

•

They used correlations long after anosmia symptoms were well documented in the media.

•

We demonstrated significant issues with digital surveillance during such a high interest event.

•

A large signal is required to overcome noise introduced by searches for other reasons.

Interest in COVID-19 in Latin America and the Caribbean: an infodemiological study using Google Trends

Infodemiology has been widely used to assess epidemics. In light of the recent pandemic, we use Google Search data to explore online interest about COVID-19 and related topics in 20 countries of Latin America and the Caribbean. Data from Google Trends from December 12, 2019, to April 25, 2020, regarding COVID-19 and other related topics were retrieved and correlated with official data on COVID-19 cases and with national epidemiological indicators. The Latin American and Caribbean countries with the most interest for COVID-19 were Peru (100%) and Panama (98.39%). No correlation was found between this interest and national epidemiological indicators. The global and local response time were 20.2 ± 1.2 days and 16.7 ± 15 days, respectively. The duration of public attention was 64.8 ± 12.5 days. The most popular topics related to COVID-19 were: the country's situation (100 ± 0) and coronavirus symptoms (36.82 ± 16.16). Most countries showed a strong or moderated (r = 0.72) significant correlation between searches related to COVID-19 and daily new cases. In addition, the highest significant lag correlation was found on day 13.35 ± 5.76 (r = 0.79). Interest shown by Latin American and Caribbean countries for COVID-19 was high. The degree of online interest in a country does not clearly reflect the magnitude of their epidemiological indicators. The response time and the lag correlation were greater than in European and Asian countries. Less interest was found for preventive measures. Strong correlation between searches for COVID-19 and daily new cases suggests a predictive utility.

Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries

Background

Due to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19–related health information during the pandemic.

Objective

The aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage.

Methods

To achieve the study objectives, we analyzed the public’s information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19–related “diseases,” “treatments and medical resources,” “symptoms and signs,” and “public measures.” The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage.

Results

Across all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19–related “diseases,” in most countries, the RSV of the term “coronavirus” increased earlier than that of “covid-19”; however, around April 2020, the search volume of the term “covid-19” surpassed that of “coronavirus.” Regarding the topic “treatments and medical resources,” the most and least searched terms were “mask” and “ventilator,” respectively. Regarding the topic “symptoms and signs,” “fever” and “cough” were the most searched terms. The RSV for the term “lockdown” was significantly higher than that for “social distancing” under the topic “public health measures.” In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic “treatments and medical resources” and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic “diseases” and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage.

Conclusions

Our findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics.

Do you see what I see? Images of the COVID-19 pandemic through the lens of Google

During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the "picture" of the Covid-19 pandemic accessed by users differs. We explore variations in what users "see" concerning the pandemic through Google image search, using a two-step approach. First, we crowdsource a search task to users in four regions of Europe, asking them to help us create a photo documentary of Covid-19 by providing image search queries. Analysing the queries, we find five common themes describing information needs. Next, we study three sources of variation - users' information needs, their geo-locations and query languages - and analyse their influences on the similarity of results. We find that users see the pandemic differently depending on where they live, as evidenced by the 46% similarity across results. When users expressed a given query in different languages, there was no overlap for most of the results. Our analysis suggests that localisation plays a major role in the (dis)similarity of results, and provides evidence of the diverse "picture" of the pandemic seen through Google.

Online Search Trends Influencing Anticoagulation in Patients With COVID-19: Observational Study

Background

Early evidence of COVID-19–associated coagulopathy disseminated rapidly online during the first months of 2020, followed by clinical debate about how best to manage thrombotic risks in these patients. The rapid online spread of case reports was followed by online interim guidelines, discussions, and worldwide online searches for further information. The impact of global online search trends and online discussion on local approaches to coagulopathy in patients with COVID-19 has not been studied.

Objective

The goal of this study was to investigate the relationship between online search trends using Google Trends and the rate of appropriate venous thromboembolism (VTE) prophylaxis and anticoagulation therapy in a cohort of patients with COVID-19 admitted to a tertiary hospital in Ireland.

Methods

A retrospective audit of anticoagulation therapy and VTE prophylaxis among patients with COVID-19 who were admitted to a tertiary hospital was conducted between February 29 and May 31, 2020. Worldwide Google search trends of the term “COVID-19” and anticoagulation synonyms during this time period were determined and correlated against one another using a Spearman correlation. A P value of <.05 was considered significant, and analysis was completed using Prism, version 8 (GraphPad).

Results

A statistically significant Spearman correlation (P<.001, r=0.71) was found between the two data sets, showing an increase in VTE prophylaxis in patients with COVID-19 with increasing online searches worldwide. This represents a proxy for online searches and discussion, dissemination of information, and Google search trends relating to COVID-19 and clotting risk, in particular, which correlated with an increasing trend of providing thromboprophylaxis and anticoagulation therapy to patients with COVID-19 in our tertiary center.

Conclusions

We described a correlation of local change in clinical practice with worldwide online dialogue and digital search trends that influenced individual clinicians, prior to the publication of formal guidelines or a local quality-improvement intervention.

Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India

OBJECTIVE

Digital surveillance has shown mixed results as a supplement to traditional surveillance. Google Trends™ (GT) (Google, Mountain View, CA, United States) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India.

METHODS

Data was obtained on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behavior in India between January 1 and May 31, 2020 in the form of relative search volume (RSV). We also used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests.

RESULTS

GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms "COVID 19," "COVID," "social distancing," "soap," and "lockdown" at the national level. In 5 high-burden states, high correlation was observed for these 5 terms along with "Corona." Peaks in RSV, both at the national level and in high-burden states corresponded with media coverage or government declarations on the ongoing pandemic.

CONCLUSION

The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage-induced curiosity, or health-seeking curiosity.

Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

BACKGROUND

Google Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.

METHODS

We extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.

RESULTS

Our Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by Pearson or Spearman's rank correlation-based approach. "Sense of smell" and "loss of smell" were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan).

CONCLUSIONS

Our results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.

The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

This study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

Big Data Research in Fighting COVID-19: Contributions and Techniques

The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

COVID-19 pandemic in the Middle East countries: coronavirus-seeking behavior versus coronavirus-related publications

The spread of COVID-19 has created a fundamental need for coordinated mechanisms responding to outbreaks in different sectors. One of the main sectors relates to information supply and demand in the middle of this pandemic in the digital environment. It could be called an infodemiology. It is known as a promising approach to solving the challenge in the present age. At this level, the purpose of this article is to investigate the COVID-19 related search process by field research. Data were retrieved from Google Trends in Middle Eastern countries alongside scientific research output of Middle Eastern scientists towards COVID-19 in Web of Science, Scopus, and PubMed. Daily COVID-19 cases and deaths were retrieved from the World Health Organization. We searched for descriptive statistical analyses to detect coronavirus-seeking behavior versus coronavirus releases in the Middle East in 2020. Findings show that people in the Middle East use various keyword solutions to search for COVID-19 in Google. There is a significant correlation between coronavirus confirmed cases and scientific productivity (January 2020-December 2020). Also, there is a positive association between the number of deaths and the number of scientific publications (except Jordan). It was a positive and significant association between online coronavirus-seeking behavior on Google (RSVs) and the confirmed cases (except Syria and Yemen). Furthermore, it was a positive relationship between RSVs and scientific productivity in the Middle East (except Bahrain and Qatar). From an infodemiological viewpoint, there is a significant correlation between coronavirus information demand and its information provision.

Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation

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 <10 and 187 (87.0%) had Pearson correlation coefficients >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.

Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers

Several determinants are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated the effects of the most prominent determining factors on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany's National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy) or within residential areas, and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects leading to an increase in new COVID-19 cases for public mobility in retail and recreational areas or workplaces, awareness measured by searches for "corona" in Google, higher rainfall, and some socio-demographic factors. Non-pharmaceutical interventions were found to be effective in reducing case numbers. This comprehensive causal graph analysis of a variety of determinants affecting COVID-19 progression gives strong evidence for the driving forces of mobility, public awareness, and temperature, whose implications need to be taken into account for future decisions regarding pandemic management.

Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation

Background

The COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown.

Objective

We used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available.

Methods

We used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries’ telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries’ ICT infrastructure profiles were retrieved from the World Economic Forum Report.

Results

Across the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (≥70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure.

Conclusions

There is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic.

The Popularity of the Biologically-Based Therapies During Coronavirus Pandemic Among the Google Users in the USA, UK, Germany, Italy and France

Object

The aim of this retrospective infodemiological study was to evaluate people’s interests in biologically-based (B-B) complementary and alternative medicine (CAM) therapies such as herbs, foods, and supplements during the coronavirus pandemic via analysis of Google search engine statistics.

Design & settings

The category, period, and regions selected in the Google Trends were “health,” “15 January–15 May 2020,” in the United States of America (USA), the United Kingdom (UK), Germany, Italy, and France, respectively. The most commonly searched herbs, foods and supplements (n = 32) during the pandemic were determined from a pool of keywords (n = 1286) based on the terms’ relative search volumes (RSVs) within the last five years. Correlation analyses were conducted to investigate associations between coronavirus-related parameters with each keyword’s RSV for each country. Selected keywords (n = 25) were analyzed using the gtrendsR package in the R programming language; the ggplot2 package was used to visualize the data, the Prophet package was used to estimate the time series, and the dplyr package was used to create the data frame.

Results

Significantly strong positive correlations were identified between daily RSVs of the terms “black seed,” “vitamin C,” “zinc,” and “quercetin,” and search queries for “coronavirus” and “COVID-19” in the USA (Spearman’s correlation coefficient > 0.8, p < 0.05), and between the RSVs of the terms “vitamin C” and “zinc,” and daily search queries for “coronavirus” and/or “COVID-19” in the UK (Spearman’s correlation coefficient > 0.8, p < 0.05).

Conclusion

Google Trends can be a beneficial tool for following public interest in identifying outbreak-related misinformation, and scientific studies and statements from authorities and the media play a potential role in driving internet searches.

Online Search Behavior Related to COVID-19 Vaccines: Infodemiology Study

BACKGROUND

Vaccination against COVID-19 is an important public health strategy to address the ongoing pandemic. Examination of online search behavior related to COVID-19 vaccines can provide insights into the public's awareness, concerns, and interest regarding COVID-19 vaccination.

OBJECTIVE

The aim of this study is to describe online search behavior related to COVID-19 vaccines during the start of public vaccination efforts in the United States.

METHODS

We examined Google Trends data from January 1, 2021, through March 16, 2021, to determine the relative search volume for vaccine-related searches on the internet. We also examined search query log data for COVID-19 vaccine-related searches and identified 5 categories of searches: (1) general or other information, (2) vaccine availability, (3) vaccine manufacturer, (4) vaccine side-effects and safety, and (5) vaccine myths and conspiracy beliefs. In this paper, we report on the proportion and trends for these different categories of vaccine-related searches.

RESULTS

In the first quarter of 2021, the proportion of all web-based search queries related to COVID-19 vaccines increased from approximately 10% to nearly 50% of all COVID-19-related queries (P<.001). A majority of COVID-19 vaccine queries addressed vaccine availability, and there was a particularly notable increase in the proportion of queries that included the name of a specific pharmacy (from 6% to 27%; P=.01). Queries related to vaccine safety and side-effects (<5% of total queries) or specific vaccine-related myths (<1% of total queries) were uncommon, and the relative frequency of both types of searches decreased during the study period.

CONCLUSIONS

This study demonstrates an increase in online search behavior related to COVID-19 vaccination in early 2021 along with an increase in the proportion of searches related to vaccine availability at pharmacies. These findings are consistent with an increase in public interest and intention to get vaccinated during the initial phase of public COVID-19 vaccination efforts.

Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic

Understanding attention dynamics on social media during pandemics could help governments minimize the effects. We focus on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches. We show how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced. Then very rapidly a situation was reached where COVID-related hashtags occupied 30-70% of the HSL, however, with changing content. We give an analysis of how the hashtag topics changed during the investigated time span and conclude that there are three periods separated by February 12 and March 12. In period 1, we see strong topical correlations and clustering of hashtags; in period 2, the correlations are weakened, without clustering pattern; in period 3, we see a potential of clustering while not as strong as in period 1. We further explore the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list. This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00263-0.

COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study

BACKGROUND

COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future.

OBJECTIVE

In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends.

METHODS

We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain-which is dependent on the Instituto de Salud Carlos III-regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns.

RESULTS

In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance.

CONCLUSIONS

During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.

The association between COVID-19 cases and deaths and web-based public inquiries

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Rise and Fall: Hydroxychloroquine and COVID-19 Global Trends: Interest, Political Influence, and Potential Implications

OBJECTIVE

The coronavirus disease 2019 (COVID-19) pandemic has led to rapid research and reporting on potential preventatives and treatments for the disease, including the drug hydroxychloroquine (HCQ). Despite a lack of robust evidence to support the use of HCQ for treatment of COVID-19, it was publicly endorsed and received widespread media coverage and public interest. The purpose of this report is to describe and contextualize the surges in public interest, demand, and adoption of HCQ for treatment of COVID-19 and outline implications for future public health policy and practice.

METHODS

Using national and global events and Google Trends data as a measure of public interest, we describe the timeline and trends in the emergence of, interest in, and adoption of HCQ as a treatment of COVID-19. We additionally review reports on public demand for HCQ for treatment of COVID-19 and impacts on medication access among patients with indicated uses.

RESULTS

Public interest and demand for HCQ surged in the United States and globally following endorsements from public officials and enaction of policies to facilitate off-label use of HCQ for treatment of COVID-19. Surges in demand for HCQ led to multiple documented shortages and barriers to accessing HCQ treatment for patients with indicated uses for HCQ. Although there have been reversals in policies to support HCQ use for treatment of COVID-19 in some regions, others have continued or expanded recommended uses.

CONCLUSION

Insights from the global response to HCQ and COVID-19 can be used to inform prudent decision-making in the future to prevent premature action and promote informed and equitable responses to promote public health.

[Medical applications of social networks. Specific aspects of the COVID-19 pandemic]

For years, social networks have been incorporated into the day-to-day of the majority of the population. In this context, a new area of knowledge in medicine has been developed: infodemiology. It is defined as the evaluation, with the objective of improving public health, of health-related information that users upload to the network. In addition, social networks offer many possibilities for conducting public health campaigns, accessing patients, or carrying out treatment interventions.

COVID-19 Status Differentially Affects Olfaction: A Prospective Case-Control Study

Objective

The symptoms and long-term sequelae of SARS-CoV-2 infection have yet to be determined, and evaluating possible early signs is critical to determine which patients should be tested and treated. The objective of this ongoing study is to evaluate initial and short-term rhinologic symptoms, olfactory ability, and general quality of life in patients undergoing SARS-CoV-2 testing.

Study Design

Prospective case-control.

Setting

Academic institute.

Methods

Adult patients tested for SARS-CoV-2 were prospectively enrolled and separated into positive and negative groups. Each participant completed 4 validated patient-reported outcome measures. The UPSIT (University of Pennsylvania Smell Identification Test) was distributed to patients who were SARS-CoV-2 positive.

Results

The positive group reported significantly decreased sense of smell and taste on the 22-item Sinonasal Outcome Test (SNOT-22) as compared with the negative group (mean ± SD: 3.4 ± 1.7 vs 1.2 ± 1.4, P < .001). The positive group had a much higher probability of reporting a decrease in smell/taste as “severe” or “as bad as it can be” (63.3% vs 5.8%) with an odds ratio of 27.6 (95% CI, 5.9-128.8). There were no differences between groups for overall SNOT-22 domain scores, PHQ-4 depression/anxiety (Patient Health Questionnaire−4), and 5-Level EQ-5D quality-of-life scores. Mean Self-MOQ (Self-reported Mini Olfactory Questionnaire) scores were 7.0 ± 5.6 for the positive group and 1.8 ± 4.0 for the negative group (P < .001). The mean UPSIT score was 28.8 ± 7.2 in the positive group.

Conclusion

Symptomatic patients who are SARS-CoV-2 positive report severe olfactory and gustatory dysfunction via the Self-MOQ and SNOT-22 as compared with symptomatic patients testing negative.

Anomalous asthma and chronic obstructive pulmonary disease Google Trends patterns during the COVID-19 pandemic

Background

An increase in online searches on health topics may either mirror epidemiological changes or reflect media coverage. In the context of COVID-19, this is particularly relevant, as COVID-19 symptoms may be mistaken for those of respiratory disease exacerbations. Therefore, we aimed to assess Internet search patterns on asthma and chronic obstructive pulmonary disease (COPD) in the context of COVID-19, as compared to searches on other chronic diseases.

Methods

We retrieved Google Trends (GTs) data on two respiratory (asthma and COPD) and three non-respiratory (diabetes, hypertension, and Crohn’s disease) chronic diseases over the past 5 years (up to May 31, 2020). For 54 countries, and for each disease, we built autoregressive integrated moving average (ARIMA) models to predict GTs for 2020 based on 2015–2019 search patterns. In addition, we estimated the proportion of searches in which COVID-19-related terms were used. To assess the potential impact of media coverage on online searches, we assessed whether weekly “asthma” GTs correlated with the number of Google News items on asthma.

Results

Over the past 5 years, worldwide search volumes for asthma and COPD reached their maximum values in March 2020. Such was not observed for diabetes, hypertension and Crohn’s disease. In 38 (70%) countries, GTs on asthma were higher in March 2020 than the respective maximum predicted values. This compares to 19 countries for COPD, 23 for hypertension, 11 for Crohn’s disease, and 9 for diabetes. Queries with COVID-19-related terms represented up to 47.8% of the monthly searches on asthma, and up to 21.3% of COPD searches. In most of the assessed countries, moderate-strong correlations were observed between “asthma” GTs and the number of news items on asthma.

Conclusions

During March 2020, there was a peak in searches on asthma and COPD, which was probably mostly driven by media coverage, as suggested by their simultaneity in several countries with different epidemiological situations.