Use of Google Trends to investigate loss-of-smell-related searches during the COVID-19 outbreak

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.

Google Trends Analysis of Otologic Symptom Searches Following COVID-19

Introduction

COVID-19 infection was accompanied by otologic symptoms, a pattern that was captured early by Google Trends. The objective of this study is to investigate searches for otologic symptoms and identify correlations with the pandemic onset.

Materials and Methods

Search interest for otologic symptoms was gathered using Google Trends from two years before and two years following the pandemic start date. A two-tailed Mann-Whitney U test was used to identify significant changes and effect size.

Results

In total, search interest for 14 terms was collected, with significant changes identified in 11. Six terms showed increased search interest, with the most significant rises observed for headache (r=0.589, p<0.001), dizziness (r=0.554, p<0.001), and tinnitus (r=0.410, p<0.001). Search interest decreased for five terms, with the most notable declines found in searches for migraine headache (r=0.35, p<0.001) and phonophobia (r=0.22, p=0.002). No significant changes were seen in ear pressure (p=0.142), neck pain (p=0.935), and sudden hearing loss (p=0.863) searches.

Conclusion

COVID-19 infection is often accompanied otologic symptoms and holds a diagnostic role. Fluctuating search interest may be attributed to a true increase in cases, media trends, or people's desires to stay informed. Google Trends robustly captured trends in search interest and presented itself as a valuable epidemiological tool.

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.

Monitoring SARS-CoV-2 Using Infoveillance, National Reporting Data, and Wastewater in Wales, United Kingdom: Mixed Methods Study

BACKGROUND

The COVID-19 pandemic necessitated rapid real-time surveillance of epidemiological data to advise governments and the public, but the accuracy of these data depends on myriad auxiliary assumptions, not least accurate reporting of cases by the public. Wastewater monitoring has emerged internationally as an accurate and objective means for assessing disease prevalence with reduced latency and less dependence on public vigilance, reliability, and engagement. How public interest aligns with COVID-19 personal testing data and wastewater monitoring is, however, very poorly characterized.

OBJECTIVE

This study aims to assess the associations between internet search volume data relevant to COVID-19, public health care statistics, and national-scale wastewater monitoring of SARS-CoV-2 across South Wales, United Kingdom, over time to investigate how interest in the pandemic may reflect the prevalence of SARS-CoV-2, as detected by national testing and wastewater monitoring, and how these data could be used to predict case numbers.

METHODS

Relative search volume data from Google Trends for search terms linked to the COVID-19 pandemic were extracted and compared against government-reported COVID-19 statistics and quantitative reverse transcription polymerase chain reaction (RT-qPCR) SARS-CoV-2 data generated from wastewater in South Wales, United Kingdom, using multivariate linear models, correlation analysis, and predictions from linear models.

RESULTS

Wastewater monitoring, most infoveillance terms, and nationally reported cases significantly correlated, but these relationships changed over time. Wastewater surveillance data and some infoveillance search terms generated predictions of case numbers that correlated with reported case numbers, but the accuracy of these predictions was inconsistent and many of the relationships changed over time.

CONCLUSIONS

Wastewater monitoring presents a valuable means for assessing population-level prevalence of SARS-CoV-2 and could be integrated with other data types such as infoveillance for increasingly accurate inference of virus prevalence. The importance of such monitoring is increasingly clear as a means of objectively assessing the prevalence of SARS-CoV-2 to circumvent the dynamic interest and participation of the public. Increased accessibility of wastewater monitoring data to the public, as is the case for other national data, may enhance public engagement with these forms of monitoring.

The evolution of the COVID-19 pandemic through the lens of google searches

Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Using data from 221 countries and territories, we demonstrate the capacity of Google search data to anticipate reported COVID-19 cases and understand how containment policies are associated with changes in socioeconomic indicators. First, search interest in COVID-specific symptoms such as "loss of smell" strongly correlated with cases initially, but the association diminished as COVID-19 evolved; general terms such as "COVID symptoms" remained strongly associated with cases. Moreover, trends in search interest preceded trends in reported cases, particularly in the first year of the pandemic. Second, countries with more restrictive containment policies experienced greater search interest in unemployment and mental health terms after policies were implemented, indicating socio-economic externalities. Higher-income countries experienced a larger increase in searches related to unemployment and a larger reduction in relationship and family planning keywords relative to lower-income countries. The results demonstrate that real-time search interest can be a valuable tool to inform policies across multiple stages of the pandemic.

To tweet or not to tweet in otorhinolaryngology, that is the question

OBJECTIVE

To describe the scientific medical literature devoted to the various interactions between otorhinolaryngology (ORL) and social networks.

MATERIAL AND METHODS

A literature search, conducted on December 15, 2021, using the search engine of the United States National Library of Medicine (PubMed) and the key-words "otorhinolaryngology", "social network", and "Twitter", retrieved 321 articles for which Abstracts were read for selection of articles with qualitative and quantitative data regarding the various relationships between ORL and social media.

RESULTS

Forty-four articles were selected and analyzed. Thirty-nine originated from the USA and none from France or French-speaking countries. Only 1 article was a prospective randomized study. Schematically, two main types of publication were identified. The first dealt with user interactions, topics discussed, teaching of the ORL specialty and publicizing research. The second dealt with the limits and dangers of this new means of publicizing scientific thinking in our specialty. Reading these articles highlighted the role of social media in publicizing ORL research, and suggested distinct options to improve interactions between otorhinolaryngologists, patients and society as a whole.

CONCLUSION

At a time when society is demanding rapid access to medical research findings, just as it is mandatory to master and adhere to the rules for medical writing in research, it is also now necessary to learn how to communicate via social media and send a tweet if one wishes to publicize its research and/or exchange with patients.

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.

Longitudinal Association of COVID-19 Hospitalization and Death with Online Search for Loss of Smell or Taste

Surveillance of COVID-19 is challenging but critical for mitigating disease, particularly if predictive of future disease burden. We report a robust multiyear lead-lag association between internet search activity for loss of smell or taste and COVID-19-associated hospitalization and deaths. These search data could help predict COVID-19 surges.

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

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

Effect of IUCN Red List category on public attention to mammals

Cultural data is a powerful tool to analyze public awareness of key societal issues, including the conservation of nature. I used two publicly available repositories of cultural data, Google Trends and Google Ngram, to quantify the effect of the International Union for the Conservation of Nature (IUCN) Red List conservation status on public attention toward 4539 mammal species. With Google Trends, I calculated whether Google searches for their common and scientific names have been increasing or decreasing over time. I also ran an anomaly detection analysis to investigate whether a change in red-list status directly results in an increase in Google searches. Additionally, I quantified the mentions of species' common and scientific names in English texts with Google Ngram. Overall, Google searches for most mammal species remained at similar levels or increased since 2008. The severity of species' IUCN Red List status was a significant predictor of increasing Google searches, although the effect size was relatively small. Red-list status seemed strongly confounded with mammal body size. Species that moved to a higher-risk category spiked significantly in Google searches directly after the new designation. The mention of species' common names in the Google Ngram's English 2019 corpus significantly increased as the red-list category increased. These results provide valuable insight into the importance of the IUCN Red List for increasing public awareness and the usefulness of publicly available cultural data on examining the effectiveness of specific conservation efforts and thus evaluating targets for support and funding.

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

Introduction

Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system.

Methods

The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence.

Results

Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche.

Conclusion

Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Alternative and Adjunct Treatments for Scoliosis: A Google Trends Analysis of Public Popularity Compared With Scientific Literature

Purpose As Google searches have often been found to provide inaccurate information regarding various treatments for orthopedic conditions, it becomes important to analyze search trends to understand what treatments are most popularly considered and the quality of information available. We sought to compare the public interest in popular adjunct/alternative scoliosis treatments to the published literature on these topics and assess any temporal trends in the public interest in these treatments. Methods The study authors compiled the most common adjunct/alternative treatments for scoliosis on PubMed. Chiropractic manipulation, Schroth exercises, physical therapy, pilates, and yoga, along with "scoliosis," were each entered into Google Trends, collected from 2004 to 2021. A linear regression analysis of covariance (ANCOVA) was done to determine whether there was a linear relationship between Google Trends' popularity and PubMed publication data. The seasonal popularity of the terms was assessed using locally estimated scatterplot smoothing (LOESS) regression. Results Google Trends and publication frequency linear regression curves were different for chiropractic manipulation (p < 0.001), Schroth exercises (p < 0.001), physical therapy (p < 0.001), and pilates (p = 0.003). Chiropractic manipulation (p < 0.001), Schroth exercises (p = 0.003), and physical therapy (p < 0.001) had positive trends, and yoga (p < 0.001) had a negative trend. Chiropractic manipulation and yoga were more popular in the summer and winter months. Conclusion Google Trends can provide orthopedic surgeons and other healthcare professionals with valuable information on which treatments are gaining popularity with the public, so physicians may specifically inform themselves prior to patient encounters, leading to more productive shared decision-making.

Harnessing Google Health Trends Data for Epidemiologic Research

Interest in using internet search data, such as that from the Google Health Trends Application Programming Interface (GHT-API), to measure epidemiologically relevant exposures or health outcomes is growing due to their accessibility and timeliness. Researchers enter search term(s), geography, and time period, and the GHT-API returns a scaled probability of that search term, given all searches within the specified geographic-time period. In this study, we detailed a method for using these data to measure a construct of interest in 5 iterative steps: first, identify phrases the target population may use to search for the construct of interest; second, refine candidate search phrases with incognito Google searches to improve sensitivity and specificity; third, craft the GHT-API search term(s) by combining the refined phrases; fourth, test search volume and choose geographic and temporal scales; and fifth, retrieve and average multiple samples to stabilize estimates and address missingness. An optional sixth step involves accounting for changes in total search volume by normalizing. We present a case study examining weekly state-level child abuse searches in the United States during the coronavirus disease 2019 pandemic (January 2018 to August 2020) as an application of this method and describe limitations.

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.

Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

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.

Trends of intrauterine device insertion and 'Googling' about intrauterine devices before and during the COVID-19 pandemic in Australia

Objective

The COVID-19 pandemic significantly disrupted access to primary care in Australia. This could have negatively impacted reproductive health services rates such as intrauterine device insertion rates, and interest in seeking information about intrauterine devices by searching on Google. We aimed to assess the trends of, and the association between, the actual Medicare service utilization rates for intrauterine device insertion and searching about intrauterine devices on Google, before and during the COVID-19 pandemic.

Methods

We conducted systematic analyses of secondary data from June 2017 to May 2022, using Medicare and Google Trends data sources. We visualized the rates of intrauterine device insertion, plus Google's search volumes about 'Intrauterine device' and 'Progestin IUDs' as topics. Then, we assessed the correlation between intrauterine device insertion rates and Google search, using Spearman correlation.

Results

The average yearly rates of intrauterine device insertion increased noticeably from 25.1-26.3 in 2018-2019 to 29.3-31.2 per 100,000 population in 2020-2021 (12-18% increase). The highest monthly intrauterine device insertion rate nationally (37 per 100,000 population) was seen in March 2021. By June 2020, search term use for the two intrauterine device-related topics returned to much higher levels (50% increase for 'Progestin IUDs', and 54% for 'Intrauterine device', respectively). A moderately strong correlation was seen between actual intrauterine device insertion rates and search on Google about intrauterine devices (Spearman rho = 0.61, p < 0.000).

Conclusion

We demonstrated a moderately strong correlation between trends of intrauterine device insertion rates and search on Google about intrauterine devices, before and during the COVID-19 pandemic in Australia. Googling about intrauterine devices could, therefore, be a useful indicator to gauge future interest in actual intrauterine device insertion for months thereafter.

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.

Public interest and awareness regarding general health, sleep quality and mental wellbeing during the early COVID-19 pandemic period: An exploration using Google trends

The internet network continues to be a major source of health-related information. Our research provides insights into the online health-seeking behaviors of the general population, and evaluates the potential relationship between the COVID-19 pandemic and public interest and awareness of general sleep health, mental health and wellbeing. Google Trends' weekly relative search volumes (RSVs) were examined during 2020 for searches specifically related to COVID-19 symptoms, and for searches related to general health, sleep and wellbeing, in the United Kingdom, the United States of America, France, Italy and Japan. To obtain insight into the association between the initiation of public restrictions and online search trends, we assessed a six-week period; the 'early pandemic period' (EPP) (01 March 2020 - 11 April 2020). To provide a meaningful pre-pandemic comparison, a similar period during 2019 (03 March - 13 April 2019) was compared for RSV and median difference analysis. The EPP was associated with increased online searches related to COVID-19 symptoms, as compared with those related to more general sleep health, mental health and wellbeing. The latter search terms frequently showed a decrease or minimal change in RSV during the EPP compared with the equivalent period in 2019. This finding illustrates the potential link between the COVID-19 pandemic and online search behavior and corroborates existing findings regarding internet searches during this period. Proactive communication by healthcare professionals during future pandemics and as an ongoing measure could help prevent public neglect of general health and wellbeing symptoms, and encourage reporting and early intervention.

Determining the interest in pain and analgesic during and before the covid-19 pandemic period using google trends data: an infodemiological study

Background: The aim of this study is to evaluate the public's interest in pain and painkillers using Google search activity in countries with the most cases before and during the COVID-19 pandemic (January 2018 - December 2021). Methods: United States (USA), England, France, Germany, Italy, India, Spain, Russia, Brazil and Turkey, which are the countries where the Covid 19 epidemic was most intense, were determined along with the world for the analysis. The words of "Back pain", "Chest pain", "Headache", "Knee pain", "Sore throat", "Aspirin", "Ibufren" and "Paracetamol" were written into the Google Trend search engine. RapidMiner Analysis program and Microsoft Excel program were used in the statistical analysis of the data. Correlation tests were used to determine the strength of the relationship between pain regions and drugs. Results: The terms fo "ibuprofen", "aspirin", "paracetamol" peaked in Google searches on March 15, 2020. The search frequencies of the terms of sore throat, chest pain, and headache peaked worldwide between March 15, 2020 and March 22, 2020. The strong correlations were obtained, ranging from 0.627 to 0.901 for chest pain and headache terms, and 0.629 to 0.749 for ibuprofen and paracetamol terms. Conclusion: As a result of the research, it is seen that the frequency of searching for pain and analgesics has increased significantly during the COVID-19 period. Our data can be considered as an indicative of the increasing incidence of pain with the COVID-19 pandemic, since internet searches are a proxy for the public good.

Big data and infectious disease epidemiology: A bibliometric analysis and research agenda

BACKGROUND

Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.

OBJECTIVE

This paper aims to synthesize research and identify hotspots of big data in infectious disease epidemiology.

METHODS

Bibliometric data from 3054 documents that satisfied the inclusion criteria were retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.

RESULTS

The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. It also placed the US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.

CONCLUSIONS

Proposals for future studies are made based on these findings. This study will provide healthcare informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

Olfactory training - Thirteen years of research reviewed

The sense of smell is interrelated with psychosocial functioning. Olfactory disorders often decrease quality of life but treatment options for people with olfactory loss are limited. Additionally, olfactory loss accompanies and precedes psychiatric and neurodegenerative diseases. Regular, systematic exposure to a set of odors, i.e., olfactory training (OT) has been offered for rehabilitation of the sense of smell in clinical practice. As signals from the olfactory bulb are directly projected to the limbic system it has been also debated whether OT might benefit psychological functioning, i.e., mitigate cognitive deterioration or improve emotional processing. In this review we synthesize key findings on OT utility in the clinical practice and highlight the molecular, cellular, and neuroanatomical changes accompanying olfactory recovery in people with smell loss as well as in experimental animal models. We discuss how OT and its modifications have been used in interventions aiming to support cognitive functions and improve well-being. We delineate main methodological challenges in research on OT and suggest areas requiring further scientific attention.

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.

Global monitoring of public interest in preventive measures against COVID-19 via analysis of Google Trends: an infodemiology and infoveillance study

OBJECTIVES

The COVID-19 pandemic has influenced people's concerns regarding infectious diseases and their preventive measures. However, the magnitude of the impact and the difference between countries are unclear. This study aimed to assess the magnitude of the impact of COVID-19 on public interest and people's behaviours globally in preventing infectious diseases while comparing international trends and sustainability.

DESIGN

An infodemiology and infoveillance study.

SETTING

The study employed a web-based data collection to delineate public interest regarding COVID-19 preventive measures using Google Trends.

PRIMARY AND SECONDARY OUTCOME MEASURES

A relative search volume was assigned to a keyword, standardising it from 0 to 100, with 100 representing the highest share of the term searches. The search terms "coronavirus", "wash hand", "social distancing", "hand sanitizer" and "mask" were investigated across 196 different countries and regions from July 2018 to October 2021 and weekly reports of the relative search volume were obtained. Persistence of interest was assessed by comparing the first 20 weeks with the last 20 weeks of the study period.

RESULTS

Although the relative search volume of "coronavirus" increased and was sustained at a significantly higher level (p<0.05) than before the pandemic declaration, globally, the trends and sustainability of the interest in preventable measures against COVID-19 varied between countries and regions.

CONCLUSIONS

Sustained interest in preventive measures differed globally, with regional differences noted among Asia, Europe, Africa and the Americas. The global differences should be considered for implementing effective interventions against COVID-19. The increased interest in preventive behaviours against COVID-19 may be related to overall infectious disease prevention.

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.

Utilizing Baidu Index to Investigate Seasonality, Spatial Distribution and Public Attention of Dry Eye Diseases in Chinese Mainland

Purpose

To explore the characteristics of spatial-temporal prevalence and public attention of dry eye diseases (DED) through Baidu Index (BI) based on infodemiology method.

Methods

The data about BI of DED were collected from Baidu search engine using "Dry eye diseases" as keyword. The spatial and temporal distribution of DED were analyzed through timeseries data decomposition as well as spatial autocorrelation and hotspot detection of BI about DED. The most popular related words and demographic characteristics were recorded to determine the public attention of DED.

Results

The trends of BI about DED in Chinese mainland had gradually increased over time with a rapid increase from 2012 to 2014 and in 2018. The results of timeseries decomposition indicated that there was seasonality in the distribution of BI about DED with the peak in winter, especially in northern regions. The geographic distribution demonstrated the search activities of DED was highest in the east of Chinese mainland while lowest in the west. The vast majority of people searching for DED were teenagers (20-29 years), with a predominance of females. Glaucoma, keratitis and conjunctivitis were the diseases most often confused with DED, and the artificial tears were the most common treatment for DED in Chinese mainland according to the BI about DED.

Conclusions

The analysis revealed the seasonality, geographic hotspots and public concern of DED through BI in Chinese mainland, which provided new insights into the epidemiology of DED.

The COVID-19 Pandemic and the Interest in Prayer and Spirituality in Poland According to Google Trends Data in the CONTEXT of the Mediatisation of Religion Processes

The research undertaken in this article uses the Google Trends tool to study the degree of interest in prayer and general spirituality during the initial phase of the COVID-19 pandemic in Poland and Europe. The authors assumed that for people interested in prayer during the COVID-19 pandemic, the Internet served as a virtual prayer book. The main research questions addressed the frequency of typed queries, referring not only to the word “prayer” but also to specific types of prayer. In addition, interest in prayer was compared with interest in the word “prophecy” to explore the relationship between religiosity and interest in the supernatural sphere in its broadest sense. The analysis shows that there is distinct recurrence regarding the terms searched, with some of them noticeably intensifying with the outbreak of the COVID-19 pandemic. The findings also show that keywords related to prophecies were searched more frequently at significant moments in Polish history (2005—the death of John Paul II, 2010—the plane crash in which the President of Poland died) than in the months of 2020 when the pandemic struck and escalated. At that time, searches related to religion were more frequent. It can also be concluded that the outbreak of the pandemic contributed to an increase in the religious activity of Poles. The article is interdisciplinary in nature, referring primarily to Religion Studies and Mass Media and Communication Studies.

COVID-19 forecasts using Internet search information in the United States

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as “loss of taste”, we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast.

Excess Google Searches for Child Abuse and Intimate Partner Violence During the COVID-19 Pandemic: Infoveillance Approach

BACKGROUND

The COVID-19 pandemic has created environments with increased risk factors for household violence, such as unemployment and financial uncertainty. At the same time, it led to the introduction of policies to mitigate financial uncertainty. Further, it hindered traditional measurements of household violence.

OBJECTIVE

Using an infoveillance approach, our goal was to determine if there were excess Google searches related to exposure to child abuse, intimate partner violence (IPV), and child-witnessed IPV during the COVID-19 pandemic and if any excesses are temporally related to shelter-in-place and economic policies.

METHODS

Data on relative search volume for each violence measure was extracted using the Google Health Trends application programming interface for each week from 2017 to 2020 for the United States. Using linear regression with restricted cubic splines, we analyzed data from 2017 to 2019 to characterize the seasonal variation shared across prepandemic years. Parameters from prepandemic years were used to predict the expected number of Google searches and 95% prediction intervals (PI) for each week in 2020. Weeks with searches above the upper bound of the PI are in excess of the model's prediction.

RESULTS

Relative search volume for exposure to child abuse was greater than expected in 2020, with 19% (10/52) of the weeks falling above the upper bound of the PI. These excesses in searches began a month after the Pandemic Unemployment Compensation program ended. Relative search volume was also heightened in 2020 for child-witnessed IPV, with 33% (17/52) of the weeks falling above the upper bound of the PI. This increase occurred after the introduction of shelter-in-place policies.

CONCLUSIONS

Social and financial disruptions, which are common consequences of major disasters such as the COVID-19 pandemic, may increase risks for child abuse and child-witnessed IPV.

Trends of Influenza’s Symptoms Drug Search Terms in Indonesian-Language using Google Trends in the Covid-19 Pandemic

Covid-19 has spread globally and causes severe acute respiratory syndrome. The symptoms of covid-19 have similarities with influenza, such as cough, fever, runny nose, and sore throat. Therefore, the internet sources tend to have an increasing search related to influenza symptoms drugs. This study aims to assess the search trend of influenza symptoms drugs using google trend analysis in Indonesia. We explore google trend analysis using search terms in the Indonesian language related to influenza symptoms drugs from December 6th, 2020 to November 30th, 2021. The positive confirmed cases were obtained from the Indonesian government website https://covid19.go.id/. Our results demonstrated the increasing search terms related to influenza drug symptoms during July and August. The highest term search was “obat batuk”. The positive covid-19 confirmed cases in Indonesia increased during July and August. During the peak of the covid-19 outbreak in Indonesia in July-August 2021, there was an increase in google trends searching related to influenza’s drug symptoms.

When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies

Coronavirus disease 2019 (COVID-19) is known as a contagious disease and caused an overwhelming of hospital resources worldwide. Therefore, deciding on hospitalizing COVID-19 patients or quarantining them at home becomes a crucial solution to manage an extremely big number of patients in a short time. This paper proposes a model which combines Long-short Term Memory (LSTM) and Deep Neural Network (DNN) to early and accurately classify disease stages of the patients to address the problem at a low cost. In this model, the LSTM component will exploit temporal features while the DNN component extracts attributed features to enhance the model's classification performance. Our experimental results demonstrate that the proposed model achieves substantially better prediction accuracy than existing state-of-art methods. Moreover, we explore the importance of different vital indicators to help patients and doctors identify the critical factors at different COVID-19 stages. Finally, we create case studies demonstrating the differences between severe and mild patients and show the signs of recovery from COVID-19 disease by extracting shape patterns based on temporal features of patients. In summary, by identifying the disease stages, this research will help patients understand their current disease situation. Furthermore, it will also help doctors to provide patients with an immediate treatment plan remotely that addresses their specific disease stages, thus optimizing their usage of limited medical resources.

Early warning of COVID-19 hotspots using human mobility and web search query data

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1-2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning.

Your Money or Your Sense of Smell? A Comparative Analysis of the Sensory and Psychological Value of Olfaction

In public discourse, the sense of smell is typically characterized as the least important of the five senses. However, there are very little empirical data on this topic. Recently, much more attention has been brought to the sense of smell since olfactory dysfunction is a primary and often long-term symptom of COVID-19 infection. It was therefore of interest to expand research on the perceived value of olfaction in the current cultural condition. We developed a survey that directly compared the value of the senses of smell, hearing, and vision with each other and in relation to nine common items representing digital, material, personal, and physical commodities of varying social and emotional meaningfulness (phone, $10,000, favorite social media, online shopping, favorite streaming service, dream vacation, pet, hair, little left toe). In total, four hundred and seven female and male respondents comprising two life-stage groups (college students, general public adults) participated in our online survey study during winter–spring of 2021. The results reveal that the sense of smell was perceived as vastly less important than vision and hearing and much less valuable than various common commodities. We also found that life-stage and gender mediated our findings. For example, one-quarter of the college student respondents would give up their sense of smell in order to keep their phone and nearly half of all women would give up their sense of smell to keep their hair. Our data further illustrate that the senses of vision and hearing are valued relatively similarly. A number of questions arise from the present data and suggestions for ways in which our survey can be expanded and altered to address further research are discussed.

Interest in dentistry in early months of the COVID-19 global pandemic: A Google Trends approach

Background

In early the COVID‐19 pandemic, routine dental treatments have been delayed due to the risk of disease transmission. This delay may lead public to search for information on the Internet for a solution.

Objectives

This study aims to evaluate the public interest in dentistry in the early months of the COVID‐19 global pandemic in the selected countries.

Methods

The daily numbers of new COVID‐19 cases were recorded for China, South Korea, Italy, Germany, Russia, Ukraine and Turkey. For these countries, Internet search interest of the keyword ‘dentistry’, ‘coronavirus’, ‘COVID‐19’, ‘SARS‐CoV‐2’ and ‘pandemic’ in the early months of the COVID‐19 pandemic was evaluated by using Google Trends data.

Results

In most countries included the public Internet search interest in ‘dentistry+coronavirus+COVID‐19+SARS‐CoV‐2+pandemic’ peaked prior to the peak of new COVID‐19 cases. While a statistically significant positive correlation was observed between the number of new cases and Google Trends data in China, South Korea, Italy and Germany, a statistically significant negative correlation was observed in Turkey.

Conclusion

The peak public interest in dentistry has been prior to the peak of COVID‐19 new cases in most countries. The use of Internet data can provide useful information about pandemics and many other diseases.

Evaluation of COVID-19 trend in Iran; Population response to the recent pandemic overtime

Relative internet search volumes (RSV) is now being consider as a measurement of awareness for most of the trending topics. During the recent coronavirus disease (COVID-19) outbreak, many researchers used the RSVs to interpret the population responses to the pandemic in various ways. By using the RSVs searched by Persian language people, we demonstrated that the Iranian people increased their knowledge and awareness of COVID-19 during the early phases of the disease before the first peak. However, their relative searches about the COVID-19 and its clinical symptoms decreased gradually despite of the gradual rise of the confirmed cases. Their less tendency to seek information about the COVID-19 could be one of the possible explanation for the increasing number of confirmed cases even several days after easing the disease related lockdown.

Misinformation About and Interest in Chlorine Dioxide During the COVID-19 Pandemic in Mexico Identified Using Google Trends Data: Infodemiology Study

BACKGROUND

The COVID-19 pandemic has prompted the increasing popularity of several emerging therapies or preventives that lack scientific evidence or go against medical directives. One such therapy involves the consumption of chlorine dioxide, which is commonly used in the cleaning industry and is available commercially as a mineral solution. This substance has been promoted as a preventive or treatment agent for several diseases, including SARS-CoV-2 infection. As interest in chlorine dioxide has grown since the start of the pandemic, health agencies, institutions, and organizations worldwide have tried to discourage and restrict the consumption of this substance.

OBJECTIVE

The aim of this study is to analyze search engine trends in Mexico to evaluate changes in public interest in chlorine dioxide since the beginning of the COVID-19 pandemic.

METHODS

We retrieved public query data for the Spanish equivalent of the term "chlorine dioxide" from the Google Trends platform. The location was set to Mexico, and the time frame was from March 3, 2019, to February 21, 2021. A descriptive analysis was performed. The Kruskal-Wallis and Dunn tests were used to identify significant changes in search volumes for this term between four consecutive time periods, each of 13 weeks, from March 1, 2020, to February 27, 2021.

RESULTS

From the start of the pandemic in Mexico (February 2020), an upward trend was observed in the number of searches compared with that in 2019. Maximum volume trends were recorded during the week of July 19-25, 2020. The search volumes declined between September and November 2020, but another peak was registered in December 2020 through February 2021, which reached a maximum value on January 10. Percentage change from the first to the fourth time periods was +312.85, -71.35, and +228.18, respectively. Pairwise comparisons using the Kruskal-Wallis and Dunn tests showed significant differences between the four periods (P<.001).

CONCLUSIONS

Misinformation is a public health risk because it can lower compliance with the recommended measures and encourage the use of therapies that have not been proven safe. The ingestion of chlorine dioxide presents a danger to the population, and several adverse reactions have been reported. Programs should be implemented to direct those interested in this substance to accurate medical information.

Can parkland mitigate mental health burden imposed by the COVID-19? A national study in China

The worldwide coronavirus disease 2019 (COVID-19) pandemic has seriously affected not only physical health but also mental wellbeing (i.e mental stress and suicide intention) of numerous urban inhabitants across the globe. While many studies have elucidated urban parkland enhances and mental wellbeing of urban residents, the potential for parkland to mitigate mental health burden imposed by the COVID-19 has received no attention. This nationwide study systematically explored the association between parkland, the COVID-19 pandemic situation and mental wellbeing from 296 cities in China. The study innovatively used big data from Baidu Search Engine to assess city-level mental wellbeing, thereby enabling comparisons among cities. The results show that the provision of parkland is positively associated with mental wellbeing during the COVID-19 epidemic. For COVID-19-related indicators, the geographical distance to Wuhan city, work resumption rate, and travel intensity within the city are also positively associated with mental wellbeing, while the number of COVID-19 infections and the proportion of migrants from Hubei Province for each city are negatively associated with mental wellbeing. Last, the most important finding is that parkland reduces the negative effect of COVID-19 on mental wellbeing during the COVID-19 epidemic. To achieve the goal of promoting mental wellbeing through urban planning and design during the future pandemics, policymakers and planners are advised to provide more well-maintained and accessible parkland and encourage residents to use them with proper precautions.

Did the COVID-19 pandemic impact help-seeking behavior for seizure management? A Google Trends™ study

Google Trends™ is a popular tool for analyzing healthcare-seeking patterns based on observed changes in the relative search volume (RSV) of the queries made on the Google™ search engine. Google Trends™ was increasingly utilized during the pandemic to assess the impact on mental health, risk communication, the impact of media coverage, and preparedness prediction. The objective of this study was to evaluate the impact of the Coronavirus disease 2019 (COVID-19) pandemic on help-seeking behaviors for seizures and/or epilepsy by assessing the changes in seizure-related online queries in periods before and since the advent of the COVID-19 pandemic on Google Trends™. We compared the RSV volumes in the year prior to and during the COVID-19 pandemic against weekly COVID-19 positive cases for each state and US census regions Search terms were categorized according to seizure symptoms or seizure treatment. Our study showed no significant increase in the RSV for seizure and epilepsy-related searches during the COVID-19 pandemic via Google Trends™. Public health entities and medical systems may use Google Trends ™ as a way to predict national, regional, and local patient needs and drive resources to meet patient demands.

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.

In crisis, we pray: Religiosity and the COVID-19 pandemic

In times of crisis, humans have a tendency to turn to religion for comfort and explanation. The COVID-19 pandemic is no exception. Using daily and weekly data on Google searches for 107 countries, this research demonstrates that the COVID-19 crisis resulted in a massive rise in the intensity of prayer. During the early months of the pandemic, Google searches for prayer relative to all Google searches rose by 30%, reaching the highest level ever recorded. A back-of-the-envelope calculation shows that by April 1, 2020, more than half of the world population had prayed to end the coronavirus. Prayer searches remained 10% higher than previously throughout 2020, particularly so in Europe and the Americas. Prayer searches rose more among the more religious, rose on all continents, at all levels of income, inequality, and insecurity, and for all types of religion, except Buddhism. The increase is not merely a substitute for services in the physical churches that closed down to limit the spread of the virus. Instead, the rise is due to an intensified demand for religion: People pray to cope with adversity. The results thus reveal that religiosity has risen globally due to the pandemic with potential direct long-term consequences for various socio-economic outcomes.

The impact of the Covid-19 pandemic on the business interruption insurance demand in the United States

The article investigates the Covid-19 pandemic related changes in the demand for insurance services in the Unites States due to business interruptions by employing panel vector autoregression models to a dynamic panel data set of 50 states and District of Columbia for three periods of time: 01 January, 2004 to 28 June, 2020; 01 January, 2004 to January 21, 2020 (pre-Covid period); January 22, 2020 to June 28, 2020 (Covid-period). This paper is the first attempt to obtain estimates by applying Google Trends with a search key word "Business Interruption Insurance". The data was collected and reduced to a single scale by US states within the widest possible time span. Google Trends Hits and Initial Claims for Unemployment Insurance Benefits are used as endogenous variables in the built models. In the constructed models, the impact of the exogenous variable New Covid Cases is compared with that of over US billion-dollar natural disasters. The impulse responses show a positive relationship between the Google Trends Hits and Initial Claims with the Covid-factor having a significant impact on the responses. The conducted analyses reveal that the demand for insurance services due to the Covid-19 outbreak in the United States can be expected to increase 2-6 times, with the total amount of the incurred costs for the economy due to the virus ranging from 0.3 to 7 percent of the US-2019 GDP. The results lay the foundation for recommending the insurance market participants to lobby for adoption of public-private protection schemes being able to secure a more efficient response to the pandemic-related losses that may occur in the future.

Analyzing citizens' and healthcare professionals' searches for smell/taste disorders and coronavirus in Finland during the COVID-19 pandemic: Infodemiological approach using database logs

Background

The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been utilized to establish models for detecting the pandemic. However, many sources contain unreliable health information on COVID-19 and its symptoms, and platforms cannot characterize the users performing searches. Prior studies have assessed symptom searches from general search engines (Google/Google Trends). Little is known about how modeling log data on smell/taste disorders and coronavirus from the dedicated internet databases used by citizens and health care professionals (HCPs) could enhance disease surveillance. Our material and method provide a novel approach to analyze web-based information seeking to detect infectious disease outbreaks.

Objective

The aim of this study was (1) to assess whether citizens’ and professionals’ searches for smell/taste disorders and coronavirus relate to epidemiological data on COVID-19 cases, and (2) to test our negative binomial regression modeling (ie, whether the inclusion of the case count could improve the model).

Methods

We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) between December 30, 2019, and November 30, 2020 (49 weeks). Two major medical internet databases in Finland were used: Health Library (HL), a free portal aimed at citizens, and Physician’s Database (PD), a database widely used among HCPs. Log data from databases were combined with register data on the numbers of COVID-19 cases reported in the Finnish National Infectious Diseases Register. We used negative binomial regression modeling to assess whether the case numbers could explain some of the dynamics of searches when plotting database logs.

Results

We found that coronavirus searches drastically increased in HL (0 to 744,113) and PD (4 to 5375) prior to the first wave of COVID-19 cases between December 2019 and March 2020. Searches for smell disorders in HL doubled from the end of December 2019 to the end of March 2020 (2148 to 4195), and searches for taste disorders in HL increased from mid-May to the end of November (0 to 1980). Case numbers were significantly associated with smell disorders (P<.001) and taste disorders (P<.001) in HL, and with coronavirus searches (P<.001) in PD. We could not identify any other associations between case numbers and searches in either database.

Conclusions

Novel infodemiological approaches could be used in analyzing database logs. Modeling log data from web-based sources was seen to improve the model only occasionally. However, search behaviors among citizens and professionals could be used as a supplementary source of information for infectious disease surveillance. Further research is needed to apply statistical models to log data of the dedicated medical databases.

When in doubt, Google it: distress-related information seeking in Italy during the COVID-19 pandemic

BACKGROUND

Psychological health has been one of the aspects affected by the recent COVID-19 pandemic. We aim to evaluate the patterns of Google search for mental distress symptoms of Italian citizens during the various phases of the COVID-19 pandemic.

METHODS

We assessed Google searches for psychological-health related words. We gathered and analyzed data on daily search queries on depression, anxiety, and insomnia from Google Trends, in a time ranging from the Pre-COVID phase (beginning 25th January 2020) up to the second wave phase (ending 17th October 2020). We performed three general linear models on search trends of the three words and tested whether and to what extent official data about new cases of COVID-19, information searching on new cases, and the government health measures impacted on these trends.

RESULTS

Average daily search queries were higher for anxiety, followed by depression and insomnia. General linear models performed to assess differences in daily search queries for anxiety, depression and insomnia were significant, respectively [F(13, 253) = 6.80, P < .001]; [F(13, 253) = 10.25, P < .001]; [F(13, 253) = 6.61, P < .001]. Specifically, daily search queries differed among different phases of managing the COVID-19 outbreak: anxiety [F(5, 253) = 10.35, P < .001, [Formula: see text] = .17]; depression [F(5, 253) = 13.59, P < .001, [Formula: see text] = .21]; insomnia [F(5, 253) = 3.52, P = .004, [Formula: see text] = .07].

CONCLUSIONS

Our study contributed to the investigation of online information-seeking behaviors of Italians regarding mental health throughout the entire phase of the pandemic and provides insights on the possible future trends of mental distress during upcoming pandemic phases.

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.

Assessing the extent and timing of chemosensory impairments during COVID-19 pandemic

Chemosensory impairments have been established as a specific indicator of COVID-19. They affect most patients and may persist long past the resolution of respiratory symptoms, representing an unprecedented medical challenge. Since the SARS-CoV-2 pandemic started, we now know much more about smell, taste, and chemesthesis loss associated with COVID-19. However, the temporal dynamics and characteristics of recovery are still unknown. Here, capitalizing on data from the Global Consortium for Chemosensory Research (GCCR) crowdsourced survey, we assessed chemosensory abilities after the resolution of respiratory symptoms in participants diagnosed with COVID-19 during the first wave of the pandemic in Italy. This analysis led to the identification of two patterns of chemosensory recovery, partial and substantial, which were found to be associated with differential age, degrees of chemosensory loss, and regional patterns. Uncovering the self-reported phenomenology of recovery from smell, taste, and chemesthetic disorders is the first, yet essential step, to provide healthcare professionals with the tools to take purposeful and targeted action to address chemosensory disorders and their severe discomfort.

Social Data: An Underutilized Metric for Determining Participation in COVID-19 Vaccinations

Many measures have been taken since late 2019 to combat the coronavirus disease (COVID-19) pandemic. National, state, and local governments employed precautions, including mask mandates, stay-at-home orders, and social distancing policies, to alleviate the burden on healthcare workers and slow the spread of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) virus until an efficacious vaccine was made widely available. By early spring of 2021, three effective and well-tolerated SARS-CoV-2 vaccines emerged and underwent broad distribution. Throughout the course of the COVID-19 vaccination campaign, several key logistical and psychological issues surfaced. Of these, access to vaccines and vaccination hesitancy are cited as two substantial hindrances towards vaccination. Noting the demand for the SARS-CoV-2 vaccine and its highly sensitive storage requirements, accurate dose allocation is critical for vaccinating the population quickly and successfully. Here, we propose the use of social data as a tool to predict vaccination participation by correlating Google searches with state-level daily vaccination. We identified a temporal and regionally-ubiquitous Google search syntax that broadly captures daily vaccination trends. By correlating trends in the search syntax with daily vaccination rates, we were able to quantify the correlation and identify optimal lag periods between Google searches and daily vaccination. This work highlights the importance of analyzing social data as a metric to effectively arrange vaccination roll-outs, identify voluntary vaccination participation, and identify inflection points in vaccination participation. In addition, social data assessments can help direct dose allocation, identify geographic areas that may seek, but lack, access to the vaccines, and actively prepare for fluctuations in vaccination demands.