More diseases tracked by using Google Trends

Ophthalmic care may not align with patient need: An analysis on state-wide patient needs and provider density between 2008 and 2022

PURPOSE

This study aims to assess the extent to which the demand for ophthalmologic care among patients at the state level is reflected in Google Trends data, serving as an indicator of patient desire in ophthalmology.

METHODS

For each state, patient interest in ophthalmologic care was estimated using the Google Trends resource measuring web search and YouTube search rates for multiple ophthalmologic terms. We compared the change in search for ophthalmologic terms over time and used ordinary least squares regression to evaluate whether search interest for ophthalmologic terms was able to predict the rate of practicing ophthalmologists in each state. We also compare the changing rates of searches across the web and YouTube to evaluate the resources patients are most likely to utilize.

RESULTS

From 2008 to 2022, web search rates for general ophthalmology related terms increased by 43.98%, while search interest for retinal specific terms increased by 19.51%. YouTube specific results for general ophthalmology terms increased by 55.83% while search for retinal terms fell by 58.48%. Ophthalmologic and retinal specific search interest was not significantly associated with either outcome.

CONCLUSIONS

Our findings suggest that patient information needs, demographic elements, and the educational backgrounds of residents and fellows - those important factors - are surprisingly poorly correlated with ophthalmology provider density. Furthermore, we observed no noteworthy correlation between the search interest in ophthalmology and the overall density of ophthalmologists or retinal specialists. This implies that there is a pressing need to explore and implement strategies aimed at better aligning these influencing factors the choices made by ophthalmologists in selecting their practice locations to bridge the gap between healthcare availability and public interest.

Scabies in Spain: Trends in Public Interest and Ectoparasiticide Use

Scabies is an ectoparasitic dermatosis caused by the Sarcoptes scabiei var. hominis mite, which lives and reproduces in humans. Its incidence in Spain has increased in recent years. The aim of this study was to complement existing evidence on the increasing number of scabies cases in our country by analyzing changes in ectoparasiticide prescriptions and Internet searches for scabies infestations measured by Google Trends. We also examined correlations between these two variables. Our results show that public interest in scabies has increased in recent years and is positively and significantly correlated with an increasing use of ectoparasiticides. We believe that Google Trends should be considered as a complementary tool for monitoring real-time trends in scabies infestations in Spain.

[Translated article] Scabies in Spain: Trends in Public Interest and Ectoparasiticide Use

Scabies is an ectoparasitic dermatosis caused by the Sarcoptes scabiei var. hominis mite, which lives and reproduces in humans. Its incidence in Spain has increased in recent years. The aim of this study was to complement existing evidence on the increasing number of scabies cases in our country by analyzing changes in ectoparasiticide prescriptions and Internet searches for scabies infestations measured by Google Trends. We also examined correlations between these two variables. Our results show that public interest in scabies has increased in recent years and is positively and significantly correlated with an increasing use of ectoparasiticides. We believe that Google Trends should be considered as a complementary tool for monitoring real-time trends in scabies infestations in Spain.

Online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP): an infodemiology study

BACKGROUNDS

Previous studies have indicated that users' health information-seeking behavior can serve as a reflection of current health issues within a community. This study aimed to investigate the online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP).

METHODS

Google Trends (GTr) was utilized to collect big data from the internet searches conducted by Iranian web users. A focus group discussion was employed to identify users' selected keywords when searching for HSP. Additionally, keywords related to the disease's symptoms were selected based on recent clinical studies. All keywords were queried in GTr from January 1, 2012 to October 30, 2022. The outputs were saved in an Excel format and analyzed using SPSS.

RESULTS

The highest and lowest search rates of HSP were recorded in winter and summer, respectively. There was a significant positive correlation between HSP search rates and the terms "joint pain" (P = 0.007), "vomiting" (P = 0.032), "hands and feet swelling" (P = 0.041) and "seizure" (P < 0.001).

CONCLUSION

The findings were in accordance with clinical facts about HSP, such as its seasonal pattern and accompanying symptoms. It appears that the information-seeking behavior of Iranian users regarding HSP can provide valuable insights into the outbreak of this disease in Iran.

Public mass shootings cause large surges in Americans' engagement with gun policy

As public mass shootings continue to plague the United States, a growing scholarly literature seeks to understand the political effects of these tragic events. This literature, however, focuses on public opinion or turnout and vote choice, leaving open to question whether or not public mass shootings affect a range of other important actions citizens may take to engage with gun policy. Leveraging the as-good-as random timing of high-publicity public mass shootings over the past decade and an immense array of publicly available and proprietary data, we demonstrate that these events consistently cause surges in public engagement with gun policy-including internet searches, streaming documentaries, discussion on social media, signing petitions, and donating to political action committees. Importantly, we document the behaviors where shootings induce polarizing upswings in engagement and those where upswings skew toward gun control. Finally, we demonstrate that low-publicity shootings largely exert little-to-no effect on our outcomes.

Using Google Trends to Estimate the Geographic Distribution of Soil-Transmitted Helminthiasis in the United States from 2016 to 2021

Soil-transmitted helminth infections are assumed to be uncommon in the US, despite numerous studies in the past few decades showing high burdens in Appalachia and the southern states. We assessed trends of interest in the Google search engine to gauge spatiotemporal patterns of potential soil-transmitted helminth transmission. We conducted a further ecological study comparing Google search trends to risk factors for soil-transmitted helminth transmission. Google search trends for terms related to soil-transmitted helminths were clustered in Appalachia and the south, with seasonal surges suggestive of endemic transmission for hookworm, roundworm (Ascaris), and threadworm. Furthermore, lower access to plumbing, increased septic tank use, and more rural environments were associated with increased soil-transmitted helminth-related Google search terms. Together, these results suggest that soil-transmitted helminthiasis remains endemic in parts of Appalachia and the south.

Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study

BACKGROUND

Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics.

OBJECTIVE

The goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks).

METHODS

We present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity.

RESULTS

Our results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease.

CONCLUSIONS

The methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks.

State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data

BACKGROUND

Across each state, the emergence of the COVID-19 pandemic in the United States was marked by policies and rhetoric that often corresponded to the political party in power. These diverging responses have sparked broad ongoing discussion about how the political leadership of a state may affect not only the COVID-19 case numbers in a given state but also the subjective individual experience of the pandemic.

OBJECTIVE

This study leverages state-level data from Google Search Trends and Centers for Disease Control and Prevention (CDC) daily case data to investigate the temporal relationship between increases in relative search volume for COVID-19 symptoms and corresponding increases in case data. I aimed to identify whether there are state-level differences in patterns of lag time across each of the 4 spikes in the data (RQ1) and whether the political climate in a given state is associated with these differences (RQ2).

METHODS

Using publicly available data from Google Trends and the CDC, linear mixed modeling was utilized to account for random state-level intercepts. Lag time was operationalized as number of days between a peak (a sustained increase before a sustained decline) in symptom search data and a corresponding spike in case data and was calculated manually for each of the 4 spikes in individual states. Google offers a data set that tracks the relative search incidence of more than 400 potential COVID-19 symptoms, which is normalized on a 0-100 scale. I used the CDC's definition of the 11 most common COVID-19 symptoms and created a single construct variable that operationalizes symptom searches. To measure political climate, I considered the proportion of 2020 Trump popular votes in a state as well as a dummy variable for the political party that controls the governorship and a continuous variable measuring proportional party control of federal Congressional representatives.

RESULTS

The strongest overall fit was for a linear mixed model that included proportion of 2020 Trump votes as the predictive variable of interest and included controls for mean daily cases and deaths as well as population. Additional political climate variables were discarded for lack of model fit. Findings indicated evidence that there are statistically significant differences in lag time by state but that no individual variable measuring political climate was a statistically significant predictor of these differences.

CONCLUSIONS

Given that there will likely be future pandemics within this political climate, it is important to understand how political leadership affects perceptions of and corresponding responses to public health crises. Although this study did not fully model this relationship, I believe that future research can build on the state-level differences that I identified by approaching the analysis with a different theoretical model, method for calculating lag time, or level of geographic modeling.

The Relationship between Searches for COVID-19 Vaccines and Dynamics of Vaccinated People in Poland: An Infodemiological Study

BACKGROUND

Google Trends has turned out to be an appropriate tool for evaluating correlations and prognostic modelling regarding infectious diseases. The possibility of selecting a vaccine against COVID-19 has increased social interest in particular vaccines. The objective of this study was to show dependencies between the frequency of searches for COVID-19 vaccinations and the number of vaccinated people in Poland, along with epidemiological data.

METHODS

Data were collected regarding Google searches for COVID-19 vaccines, the number of people in Poland vaccinated against COVID-19, the number of new cases, and the number of deaths due to COVID-19. Data were filtered from 27 December 2020 to 1 September 2021.

RESULTS

The number of new vaccinations smoothed per million correlated most strongly with searches for the word 'Pfizer' in Google Trends (Kendall's tau = 0.46, p < 0.001). The number of new deaths correlated most strongly with the search phrase 'AstraZeneca' (Kendall's tau = 0.46, p < 0.001). The number of new cases per million correlated most strongly with searches for 'AstraZeneca' (Kendall's tau = 0.49, p < 0.001). The maximum daily number of searches ranged between 110 and 130. A significant interest in COVID-19 vaccines was observed from February to June 2021, i.e., in the period of a considerable increase in the number of new cases and new deaths due to COVID-19.

CONCLUSIONS

A significant increase in interest in COVID-19 vaccines was observed from February to June 2021, i.e., in the period of gradually extended access to vaccinations, as well as a considerable increase in the number of new cases and new deaths due to COVID-19. The use of Google Trends with relevant keywords and a comparison with the course of the COVID-19 pandemic facilitates evaluation of the relationship between the frequency and types of searches for COVID-19 vaccines and epidemiological data.

Harnessing the Potential of Google Searches for Understanding Dynamics of Intimate Partner Violence Before and After the COVID-19 Outbreak

Most social phenomena are inherently complex and hard to measure, often due to under-reporting, stigma, social desirability bias, and rapidly changing external circumstances. This is for instance the case of Intimate Partner Violence (IPV), a highly-prevalent social phenomenon which has drastically risen in the wake of the COVID-19 pandemic. This paper explores whether big data-an increasingly common tool to track, nowcast, and forecast social phenomena in close-to-real time-might help track and understand IPV dynamics. We leverage online data from Google Trends to explore whether online searches might help reach "hard-to-reach" populations such as victims of IPV using Italy as a case-study. We ask the following questions: Can digital traces help predict instances of IPV-both potential threat and actual violent cases-in Italy? Is their predictive power weaker or stronger in the aftermath of crises such as COVID-19? Our results suggest that online searches using selected keywords measuring different facets of IPV are a powerful tool to track potential threats of IPV before and during global-level crises such as the current COVID-19 pandemic, with stronger predictive power post outbreaks. Conversely, online searches help predict actual violence only in post-outbreak scenarios. Our findings, validated by a Facebook survey, also highlight the important role that socioeconomic status (SES) plays in shaping online search behavior, thus shedding new light on the role played by third-level digital divides in determining the predictive power of digital traces. More specifically, they suggest that forecasting might be more reliable among high-SES population strata.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10680-022-09619-2.

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.

Using Google Health Trends to investigate COVID-19 incidence in Africa

The COVID-19 pandemic has caused over 500 million cases and over six million deaths globally. From these numbers, over 12 million cases and over 250 thousand deaths have occurred on the African continent as of May 2022. Prevention and surveillance remains the cornerstone of interventions to halt the further spread of COVID-19. Google Health Trends (GHT), a free Internet tool, may be valuable to help anticipate outbreaks, identify disease hotspots, or understand the patterns of disease surveillance. We collected COVID-19 case and death incidence for 54 African countries and obtained averages for four, five-month study periods in 2020–2021. Average case and death incidences were calculated during these four time periods to measure disease severity. We used GHT to characterize COVID-19 incidence across Africa, collecting numbers of searches from GHT related to COVID-19 using four terms: ‘coronavirus’, ‘coronavirus symptoms’, ‘COVID19’, and ‘pandemic’. The terms were related to weekly COVID-19 case incidences for the entire study period via multiple linear and weighted linear regression analyses. We also assembled 72 variables assessing Internet accessibility, demographics, economics, health, and others, for each country, to summarize potential mechanisms linking GHT searches and COVID-19 incidence. COVID-19 burden in Africa increased steadily during the study period. Important increases for COVID-19 death incidence were observed for Seychelles and Tunisia. Our study demonstrated a weak correlation between GHT and COVID-19 incidence for most African countries. Several variables seemed useful in explaining the pattern of GHT statistics and their relationship to COVID-19 including: log of average weekly cases, log of cumulative total deaths, and log of fixed total number of broadband subscriptions in a country. Apparently, GHT may best be used for surveillance of diseases that are diagnosed more consistently. Overall, GHT-based surveillance showed little applicability in the studied countries. GHT for an ongoing epidemic might be useful in specific situations, such as when countries have significant levels of infection with low variability. Future studies might assess the algorithm in different epidemic contexts.

Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study

BACKGROUND

The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies.

OBJECTIVE

This study aimed to map depression search intent in the United States based on internet-based mental health queries.

METHODS

Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: "feeling sad," "depressed," "depression," "empty," "insomnia," "fatigue," "guilty," "feeling guilty," and "suicide." Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: "sports," "news," "google," "youtube," "facebook," and "netflix." Heat maps of population depression were generated based on search intent.

RESULTS

Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South.

CONCLUSIONS

The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States.

Monitoring Temporal Trends in Internet Searches for “Ticks” across Europe by Google Trends: Tick–Human Interaction or General Interest?

Simple Summary

It is notoriously difficult to track the spread of vector-borne diseases, and to determine the underlying causes of change in disease occurrence. Importantly, public health representatives have limited access to information, which permit them to assess trends and changes in vector–human contact. Exposed individuals may, however, seek information on the Internet and thereby deliver input to a search record for the given vector. The variation in search-frequencies may reflect the variation in contact rates, but varying general interest could also influence the data. We here investigated records for search terms synonymous with “tick(s)”, and found that the records reflect the seasonal variation, which one would expect when these result from tick encounters. Albeit, variable use of search terminology suggest that these records should not be used to make comparisons between years.

Abstract

Monitoring vector–human interaction is pivotal for assessing potential transmission rates of vector borne diseases and their associated public health impact. People often seek information following an insect bite in order to identify hematophagous arthropods, which in recent years often is done using Internet resources. Through this activity, a record of net searches is generated, which include information that reflect local human–arthropod interaction, e.g., for the common tick (Ixodes ricinus) in European countries. Such records could in principle provide low cost real-time monitoring data, if indeed Internet search activities adequately reflect tick–human interaction. We here explore Google Trends records for within-year and between-year trends, for four different Danish search terms for “tick(s)”. We further assess the relationship between monthly search-frequencies and local weather conditions (temperatures and precipitation from 2007 to 2016) in nine European countries. Our findings point to significant limitations in the records due to changes in search-term preferences over the given years. However, the seasonal dynamics are comparable among search-terms. Moreover, the seasonal pattern in search terms vary across Europe in tune with changes in temperature and precipitation. We conclude that, the within-year variation for given search-terms provide credible information, which systematically vary with local weather patterns. We are not convinced that these records merely reflect general interest. It will, however, require a more in-depth analysis by researchers that have specific insight into local language practices to fully assess the strength and weaknesses of this approach

Trends and Seasonality of Information Searches Carried Out through Google on Nutrition and Healthy Diet in Relation to Occupational Health: Infodemiological Study

This study aimed to analyze and relate the population interest through information search trends on Nutrition and Healthy Diet (HD) with the Occupational Health (OH). Ecological and correlational study of the Relative Search Volume (RSV) obtained from Google Trends query, segmented in two searched periods concerning antiquity; date of query: 20 April 2021. The RSV trends for the analyzed three Topics were: Nutrition (R² = 0.02), HD (R² = 0.07) and OH (R² = -0.72). There was a good positive correlation between Nutrition and OH (R = 0.56, p < 0.001) and a moderate one between HD and OH (R = 0.32, p < 0.001). According to seasons, differences were verified between RSV means in the Topics HD (p < 0.01) and OH (p < 0.001). Temporal dependence was demonstrated on Nutrition searches (Augmented Dickey-Fuller = -2.35, p > 0.05). There was only a significant relationship between the RSV Topic HD (p < 0.05) for the Developing and Least Developed countries. The data on the analyzed RSV demonstrated diminishing interest in the search information on HD and OH as well as a clearly positive trend change in recent years for Nutrition. A good positive correlation was observed between the RSV of nutrition and OH whereas the correlation between HD and OH was moderate. There were no milestones found that may report a punctual event leading to the improvement of information searches. Temporal dependence was corroborated in the RSV on Nutrition, but not in the other two Topics. Strangely, only an association was found on HD searches between the Developing and Least Developed Countries. The study of information search trends may provide useful information on the population's interest in the disease data, as well as would gradually allow the analysis of differences in popularity, or interest even between different countries. Thus, this information might be used as a guide for public health approaches regarding nutrition and a healthy diet at work.

Correlation Between Voluntary HIV Testing and Public Awareness of HIV Using Google Trends in Japan

The aim of this article was to evaluate the correlation between the number of voluntary human immunodeficiency virus (HIV) tests conducted and public awareness according to Internet search volumes from Google Trends. We used bivariate correlations to assess the relationship between the Internet search frequency for each term of HIV and acquired immunodeficiency syndrome (AIDS) and the number of HIV tests, by time series and prefecture. We observed positive correlations between search frequencies of the terms HIV and AIDS and the number of HIV tests in both time-series analysis (HIV: r = 0.65, P < .001; AIDS: r = 0.73, P < .001) and analysis by prefecture (HIV: r = 0.71, P < .001; AIDS: r = 0.40, P = .001). The findings of this study indicate that the current stagnant number of HIV tests conducted in Japan reflects declining public awareness regarding HIV.

Common communicable diseases in the general population in France during the COVID-19 pandemic

In France, social distancing measures have been adopted to contain the spread of COVID-19, culminating in national Lockdowns. The use of hand washing, hydro-alcoholic rubs and mask-wearing also increased over time. As these measures are likely to impact the transmission of many communicable diseases, we studied the changes in common infectious diseases incidence in France during the first year of COVID-19 circulation. We examined the weekly incidence of acute gastroenteritis, chickenpox, acute respiratory infections and bronchiolitis reported in general practitioner networks since January 2016. We obtained search engine query volume for French terms related to these diseases and sales data for relevant drugs over the same period. A periodic regression model was fit to disease incidence, drug sales and search query volume before the COVID-19 period and extrapolated afterwards. We compared the expected values with observations made in 2020. During the first lockdown period, incidence dropped by 67% for gastroenteritis, by 79% for bronchiolitis, by 49% for acute respiratory infection and 90% for chickenpox compared to the past years. Reductions with respect to the expected incidence reflected the strength of implemented measures. Incidence in children was impacted the most. Reduction in primary care consultations dropped during a short period at the beginning of the first lockdown period but remained more than 95% of the expected value afterwards. In primary care, the large decrease in reported gastroenteritis, chickenpox or bronchiolitis observed during the period where many barrier measures were implemented imply that the circulation of common viruses was reduced and informs on the overall effect of these measures.

Will I die of coronavirus? Google Trends data reveal that politics determine virus fears

Is Google Trends (GT) useful to survey populations? Extant work has shown that certain search queries reflect the attitudes of hard-to-survey populations, but we do not know if this extends to the general population. In this article, we leverage abundant data from the Covid-19 pandemic to assess whether people's worries about the pandemic match epidemiological trends as well as political preferences. We use the string 'will I die from coronavirus' on GT as the measure for people's level of distress regarding Covid-19. We also test whether concern for coronavirus is a partisan issue by contrasting GT data and 2016 election results. We find strong evidence that (1) GT search volume close matches epidemiological data and (2) significant differences exist between states that supported Clinton or Trump in 2016.

Oral Health and Teledentistry Interest during the COVID-19 Pandemic

BACKGROUND

The COVID-19 pandemic outbreak has significantly changed access to dental treatments.

METHODS

The data related to oral health and teledentistry topics were collected from the open database Google Trends. The analyzed material was collected from 19 June 2016 to 6 June 2021 among anonymous search engine users. The following expressions were analyzed: "dental care", "emergency dental care", "oral health", "periodontitis", "teledentistry", "is it safe to go to the dentist", and "COVID-19" and "PPE dentist".

RESULTS

During the first lockdown in 2020, a significant increase in "emergency dental care" phrase queries was detected, with a simultaneous decrease in regular "dental care" questions, as well as a peak in the queries for "periodontitis" preceded by lower interest in "oral health." The number of searches stated for "teledentistry" increased during the time of the pandemic 5 times and for and "PPE dentist" 30 times. The risk of visiting the dental studio was seen in almost 40 times increase in the query "is it safe to go to the dentist."

CONCLUSIONS

The COVID-19 imprinted a stigma on oral health care. In this difficult epidemiological situation, teledentistry might become a helpful solution.

An evidence-based assessment of the impact of the Olympic Games on population levels of physical activity

Pre-Olympic Games predictions commonly include an increase in population-based physical activity in the host city, as often stated in the bid, but the post-Olympic Games effects on physical activity have not been summarised. In this Series paper, we aim to do the following: examine mentions of a physical activity legacy in pre-Olympic bid documentation; analyse existing physical activity surveillance data collected before, during, and after the Olympic Games in hosting areas around the world; and evaluate Google Trends data surrounding the London 2012 Olympic Games as a case study of community interest in the topic of exercise during the time of the Olympic Games. Before 2007, little mention of physical activity was made in pre-Olympic Games documentation, but, after that, most documents had targets for population physical activity or sports participation. The synthesis of available surveillance data indicates that there was no change in the prevalence of physical activity or sports participation, except for the 2008 Summer Olympics in Beijing and the 1998 Winter Olympics in Nagano; although, the increase in participation in Nagano might not be attributable to the Olympic Games since there was no change in participation in winter sports. The Google Trends data showed an acute spike in searches with the term "Olympic" immediately associated with the London Olympic Games period and showed a sustained peri-Olympic increase in searches with the term "exercise". By themselves, the Olympic Games have not improved population-wide physical activity but might be an important missed public health opportunity. Such a legacy will require strategic planning and partnerships across the International Olympic Committee and the Olympic, sport, and public health agencies and a thorough evaluation framework implemented throughout the pre-Olympic Games and post-Olympic Games period in the host country.

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.

The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches

The unprecedented outbreaks of epidemics such as the coronavirus has caused major socio-economic changes. To analyze public risk awareness and behavior in response to the outbreak of epidemic diseases, this study focuses on RSV (Relative Search Volume) provided by Google Trends. This study uses the social big data provided by Google RSV to investigate how the WHO's pandemic declaration affected public awareness and behavior. 37 OECD countries were analyzed and clustered according to the degree of reaction to the declaration, and the United States, France and Germany were selected for comparative study. The results of this study statistically confirmed that the pandemic declaration increased public awareness and had the effect of increasing searches for information on COVID-19 by more than 20%. In addition, this rapid rise in RSV also reflected interest in the COVID-19 test and had the effect of inducing individuals to be tested, which helped identify new cases. The significance of this study is that it provided the theoretical foundation for using RSV and its implications to understand and strategically utilize public awareness and behavior in situations where the WHO and governments must launch policies in response to the outbreak of new infectious diseases such as COVID-19.

A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States Through Google Trends

PURPOSE

Our study fills the spatiotemporal gaps in dry eye disease (DED) epidemiology by using Google Trends as a novel epidemiological tool for geographically mapping DED in relation to environmental risk factors.

METHODS

We used Google Trends to extract DED-related queries estimating users' intent from 2004 to 2019 in the United States. We incorporated national climate data to generate heat maps comparing geographic, temporal, and environmental relationships of DED. Multivariable regression models were constructed to generate quadratic forecasts predicting DED and control searches.

RESULTS

Our results illustrated the upward trend, seasonal pattern, environmental influence, and spatial relationship of DED search volume across the US geography. Localized patches of DED interest were visualized in urban areas. There was no significant difference in DED queries across the US census regions (P = 0.3543). Regression model 1 predicted DED queries per state (R2 = 0.61), with the significant predictor being urban population [r = 0.56, adjusted (adj.) P < 0.001, n = 50]; model 2 predicted DED searches over time (R2 = 0.97), with significant predictors being control queries (r = 0.85, adj. P = 0.0169, n = 190), time (r = 0.96, adj. P < 0.001, n = 190), time2 (r = 0.97, adj. P < 0.001, n = 190), and seasonality (winter r = -0.04, adj. P = 0.0196, n = 190; spring r = 0.10, adj. P < 0.001, n = 190).

CONCLUSIONS

Our study used Google Trends as a novel epidemiologic approach to geographically mapping the US DED. Importantly, urban population and seasonality were stronger risk factors of DED searches than temperature, humidity, sunshine, pollution, or region. Our work paves the way for future exploration of geographic information systems for locating DED and other diseases through online population metrics.

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation (OMC), online medical appointment (OMA), and online medical search (OMS) for the regional outbreak of coronavirus disease 2019 in Shenzhen, China during January 1, 2020 to March 5, 2020. Multivariate vector autoregression models were used for the prediction. The results identified a novel predictor, OMC, which can forecast the disease trend up to 2 days ahead of the official reports of confirmed cases from the local health department. OMS data had relatively weaker predictive power than OMC in our model, and OMA data failed to predict the confirmed cases. This study highlights the importance of OMC data and has implication in providing evidence-based guidelines for local authorities to evaluate risks and allocate resources during the pandemic.

Coronavirus (Covid-19) pandemic: How may communication strategies influence our behaviours?

A novel Corona virus (SARS-CoV-2), started in Wuhan China, caused an outbreak of viral pneumonia to subsequently spread throughout the world. Italy has been one of the most affected countries in the world and the increasing number of cases and deaths has created strong emotional reactions in people. This study has aimed at evaluating public attention to this emerging disease through the use of Google Trends. Public attention, measured as the volume of internet search activity, was correlated with Health Communication Strategies and official COVID-19 data. At the moment of the study analysis, Italy was by far the first country in terms of search volume for "coronavirus" and the highest peak of searches was reached on February 23, 2020. We have found that there was a correlation between public attention to coronavirus disease and communications from Public Health policies: we observed spikes in search volumes on the days of Presidential Decree publications. Furthermore, this attention was also correlated with Case Fatality Rate (CFR). Even if CFR data are continuously updated and can be affected by patient histories, the correlation found suggests that the increase in mortality has generated growing interest in the disease and its risk perception. This study shows that tracking searches through Google Trends as a public focus indicator is a useful tool for decision-makers in guiding communication strategies and should as well stimulate a more transparent media and policy making reporting.

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: - evidence from Baidu index

BACKGROUND

New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.

METHODS

We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.

RESULTS

Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10- 6; cough: rs=0.592, p=4.485× 10- 4; fatigue: rs=0.629, p=1.494× 10- 4; sputum production: rs=0.648, p=8.206× 10- 5; shortness of breath: rs=0.656, p=6.182× 10-5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value's optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever.

CONCLUSION

The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

Insights on the implications of COVID-19 mitigation measures for mental health

Given the unprecedented level and duration of mitigation policies during the 2020 COVID-19 pandemic, it is not surprising that the public and the media have raised important questions about the potential for negative mental health consequences of the measures. To answer them, natural variability in policy implementation across US states and over time was analyzed to determine if mitigation policies correlated with Google searches for terms associated with symptoms of depression and anxiety. Findings indicated that restaurant/bar limits and stay-at-home orders correlated with immediate increases in searches for isolation and worry but the effects tapered off two to four weeks after their respective peaks. Moreover, the policies correlated with a reduction in searches for antidepressants and suicide, thus revealing no evidence of increases in severe symptomatology. The policy implications of these findings are discussed.

Oculofacial plastic surgery-related online search trends including the impact of the COVID-19 pandemic

Purpose: The authors aim to characterize oculofacial plastic surgery-related online interest that may be useful in forecasting demand and in designing patient-directed online resources. Methods: The authors queried Google Trends for over 100 oculofacial plastic surgery terms. The main outcome measure was the top 50 oculofacial plastic surgery-related search terms from 2004 to 2020. Secondary outcomes were trends, including seasonality, and search volume changes during the COVID-19 lockdown (March-May 2020) compared to 2018-2019. Terms were analyzed individually and in thematic categories; controlled against generic search terms to account for general internet traffic. Results: Between 2004 and 2020, searches for oculofacial plastic surgery altogether increased, surpassing the rate of internet traffic growth. One thematic category - eyelid malpositions - decreased month-over-month. The top five terms were "face lift," "Bell's palsy," "puffy eyes," "dark circles under eyes," and "chalazion." Eyelid neoplasms searches peaked in summer (R²  = 0.880) whereas cosmetic (R²  = 0.862), symptoms (R ² = 0.907), and surgeries (R ² = 0.140) peaked in winter. Overall, oculofacial-related searches decreased during the COVID-19 lockdown, although thyroid eye disease interest increased compared to 2018 or 2019 (+68.6%; adj. p = .005). Oculofacial plastic surgery interest in 2020 was inversely correlated to "COVID-19" searches (r = -0.76, p < .001). Conclusions: Oculofacial plastic surgery searches increased since 2004 at a pace greater than that ascribed to internet traffic growth. The most searched terms were "face lift," "Bell's palsy," "puffy eyes," "dark circles under eyes," and "chalazion." Almost all oculofacial-related searches decreased during the COVID-19 lockdown.

Can google trends search inform us about the population response and public health impact of abrupt change in alcohol policy? A case study from India during the covid-19 pandemic

Background

Sudden alcohol prohibition in India during the COVID-19 pandemic presented an opportunity to test whether Google Trends data could indicate population responses and the public health impact of alcohol policy. We hypothesized, following prohibition: there would be a significant change in the relative search volumes (RSV) of alcohol-related queries; that temporal analysis of the trends would reflect a public response to policy changes; and that geospatial analysis of RSV would correlate with the prevalence of alcohol use.

Methods

Three different search periods were used to test the hypotheses. The search inputs were based on potential public response to alcohol prohibition, as evidenced by the literature, newspaper articles, and consensus. We used RSV as the unit of analysis. Mean RSV of search queries, pre-post implementation of prohibition, were compared. Smoothing of scatter plots examined the temporal association of trends with policy measures. Multiple linear regression tested the relationship of state-wise RSV and alcohol use prevalence.

Results

Post-implementation of prohibition, a significant increase in the RSV was observed for searches related to alcohol withdrawal (p<0.001), how to extract alcohol from sanitizer (p = 0.002), alcohol home delivery online (p<0.001), alcohol home delivery (p<0.001), and sleeping pills (p = 0.006). The trends suggested a decrease in general interest in alcohol but increased demand, and a possible connection with changes in policy measures. State-level RSV and alcohol use prevalence did not reveal a significant relationship.

Conclusion

Google trend is a potential source of rapid feedback to policymakers about population responses to an abrupt change in alcohol policies.

Spatial and Temporal Comparison of Perceived Risks and Confirmed Cases of Lyme Disease: An Exploratory Study of Google Trends

Non-specific symptoms in later stages of Lyme disease (LD) may mimic a variety of autoimmune, viral, or complex diseases. Patients lacking erythema migrans or who test negative under CDC guidelines, but suspect LD may search online symptoms in vein. As a result, patients with lingering and undiagnosed symptoms turn to alternative lab tests. This study addresses patient's perceived illness in relation to CDC surveillance data. Extending the literature beyond basic searches for symptoms or disease terms, this study examines spatiotemporal dynamics among symptom, disease, and unconventional lab test searches on Google Trends, in compared with CDC confirmed cases of LD. The search terms used for the Google Trends analysis between 2011 and 2015 include: (1) "lyme" and "lyme disease" for disease, (2) "tick bite," "bone pain," "stiff neck," "circular rash," and "brain fog" for symptoms, and (3) "IGENEX" for the alternative lab test. Spatial and temporal analyses illustrate noticeable similar patterns between the search frequency and the actual LD incidence. Beyond basic searches for symptoms or disease terms, we demonstrate the improved utility of Google Trends analysis in discovering spatial and temporal patterns of perceived LD and comparing with the reported LD cases. The public health and medical communities benefit from this research through improved knowledge of undiagnosed patients who are searching for alternative labs to explain lingering symptoms. This study validates the need for further research into Google Trends data and surveillance protocols of diseases characterized by non-specific symptoms, prompting patients to "self-diagnose."

Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics

The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases.

Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study

Background

Different studies have suggested that web search data are useful in forecasting several phenomena from the field of economics to epidemiology or health issues.

Objective

This study aimed to (1) evaluate the correlation between suicide rates released by the Spanish National Statistics Institute (INE) and internet search trends in Spain reported by Google Trends (GT) for 57 suicide-related terms representing major known risks of suicide and an analysis of these results using a linear regression model and (2) study the differential association between male and female suicide rates published by the INE and internet searches of these 57 terms.

Methods

The study period was from 2004 to 2013. In this study, suicide data were collected from (1) Spain’s INE and (2) local internet search data from GT, both from January 2004 to December 2013. We investigated and validated 57 suicide-related terms already tested in scientific studies before 2015 that would be the best predictors of new suicide cases. We then evaluated the nowcasting effects of a GT search through a cross-correlation analysis and by linear regression of the suicide incidence data with the GT data.

Results

Suicide rates in Spain in the study period were positively associated (r<-0.2) for the general population with the search volume for 7 terms and negatively for 1 from the 57 terms used in previous studies. Suicide rates for men were found to be significantly different than those of women. The search term, “allergy,” demonstrated a lead effect for new suicide cases (r=0.513; P=.001). The next significant correlating terms for those 57 studied were “antidepressant,” “alcohol abstinence,” “relationship breakup” (r=0.295, P=.001; r=0.295, P=.001; and r=0.268, P=.002, respectively). Significantly different results were obtained for men and women. Search terms that correlate with suicide rates of women are consistent with previous studies, showing that the incidence of depression is higher in women than in men, and showing different gender searching patterns.

Conclusions

A better understanding of internet search behavior of both men and women in relation to suicide and related topics may help design effective suicide prevention programs based on information provided by search robots and other big data sources.

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

Background

Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.

Objective

This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.

Methods

A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.

Results

Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.

Conclusions

IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

The Google search engine answers many health and medical information queries every day. People have become used to searching for this type of information. This paper presents a study which examined the visibility of health and medical information websites. The purpose of this study was to find out why Google is decreasing the visibility of such websites and how to measure this decrease. Since August 2018, Google has been more rigorously rating these websites, since they can potentially impact people’s health. The method of the study was to collect data about the visibility of health and medical information websites in sequential time snapshots. Visibility consists of combined data of unique keywords, positions, and URL results. The sample under study was made up of 21 websites selected from 10 European countries. The findings reveal that in sequential time snapshots, search visibility decreased. The decrease was not dependent on the country or the language. The main reason why Google is decreasing the visibility of such websites is that they do not meet high ranking criteria.

Lymelight: forecasting Lyme disease risk using web search data

Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease.

Celebrity Influence Affecting Public Interest in Plastic Surgery Procedures: Google Trends Analysis

BACKGROUND

Medical decisions made by celebrities have a profound influence on medical decisions made by the general population. Google Trends (GT) is a free, online resource with virtually no barriers to use that allows for tracking of global search volumes as a proxy for determining public interest. In this study, we utilize GT to characterize the significant and measurable effects that the May 2013 announcement of Angelina Jolie's BRCA-influenced prophylactic mastectomy, May 2015 announcement of Kylie Jenner's lip augmentation, April 2017 announcement of Caitlyn Jenner's gender affirming surgery and February 2014 media attention given to Kim Kardashian's rumored buttock augmentation had on corresponding surgical procedure volumes.

METHODS

GT databases of search volumes were collected for terms related to prophylactic mastectomy, lip augmentation, gender affirming surgery and buttock augmentation categories from January 2004 to March 2019 using the "related queries" feature. Mean search volumes prior to respective announcements were compared to that of the period starting 6 months after. Additionally, the percent change from the month preceding respective celebrity announcements was compared to the month of the announcement for each search term.

RESULTS

For mastectomy, all terms demonstrated peak interest during May 2013. Following Jolie's announcement, interest in "mastectomy" rose 1328%, "prophylactic mastectomy" rose 324%, "BRCA1" rose 316%, "BRCA2" rose 138% and "BRCA gene" rose 354%. Long-term interest was higher after May 2013 than beforehand for all terms except "prophylactic mastectomy" (each, p < 0.001). Following Kylie Jenner's announcement, interest in "lip augmentation" rose 43%, "lip enhancement" rose 37%, "lip fillers" rose 3233%, "lip implants" rose 8% and "lip injections" rose 13%. Long-term interest was higher after May 2015 than beforehand for all terms except "lip augmentation" and "lip enhancement" (each, p < 0.001). Following Caitlyn Jenner's announcement, "gender affirming surgery" rose 119%, "gender reassignment" rose 186%, "gender reassignment surgery" rose 203% and "transgender surgery" rose 35%. Long-term interest was higher after April 2017 than beforehand for all terms except "sex change" (each, p < 0.001). Following Kardashian's rumored injections, interest in "butt enhancement" rose 34% and "butt implants" rose 100%. Long-term interest was higher after February 2014 than beforehand for all terms (each, p < 0.001).

CONCLUSIONS

GT data trends correlate with shifts in real-world healthcare utilization and healthcare-related public interest caused by high-profile public events, making it a useful tool for real-time prediction of trends in public health in response to a variety of observable influences.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland

Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.

Increasing Public Interest in Stem Cell Injections for Osteoarthritis of the Hip and Knee: A Google Trends Analysis

BACKGROUND

Stem cell injections are being offered to patients as a nonoperative treatment for osteoarthritis of the hip and knee. To our knowledge, no peer-reviewed data exist to document the usage frequency of these injections nor to quantify the public interest in these injections. We sought to use Google Trends to provide a quantitative analysis of interest in hip and knee stem cell injections at the population level.

METHODS

Google Trends search parameters were set to obtain query data from January 2010 through December 2017. 'Arthritis,' 'osteoarthritis,' 'stem cell,' 'injection,' 'knee,' and 'hip' were entered in various combinations to obtain the highest yield search volume. Trend analyses were performed.

RESULTS

Six linear models were generated to show trends in the volume of searches for the United States and the World. Model fit was good, and regression analysis showed significant trends over time for all searches. Use of search terms increased significantly over time (all models P < .001). Adjusted R-square values ranged from 54.4% to 78.1%. All trends showed an upward trajectory for the entirety of the study time period.

CONCLUSION

There has been a marked and statistically significant rise in search query volume related to stem cells and osteoarthritis of the hip and knee since 2010. Online interest in stem cell injections may suggest increased utilization of these procedures. Well-designed clinical studies are required to keep pace with the rising popularity and public interest in this intervention for hip and knee arthritis.

Using Google Trends Data to Study Public Interest in Breast Cancer Screening in Malaysia

Objective:

This study aims to investigate the public pattern in seeking breast cancer screening information in Malaysia using Google Trends.

Methods:

The Google Trends database was evaluated for the relative Internet search popularity of breast cancer and screening-related search terms from 2007 to 2018.

Results:

Result showed downward trends in breast cancer search, whereas mammogram and tomosynthesis search fluctuated consistently. A significant increment was found during Pink October month. Breast cancer search term achieved the highest popularity in the east coast of Malaysia with [x² (5, N=661) = 110.93, P<0.05], whereas mammogram attained the highest search volume in central Malaysia [x² (4, N=67) = 18.90, P<0.05]. The cross-correlation for breast cancer was moderate among northern Malaysia, Sabah, and Sarawak (0.3 ≤ r_s ≤ 0.7).

Conclusion:

Public interest trend in breast cancer screening is strongly correlated with the breast cancer awareness campaign, Pink October. Breast cancer screening should be promoted in the rural areas in Malaysia.

Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams-Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior

BACKGROUND

West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus.

OBJECTIVE

This study aimed at exploring the potential predictive power of West Nile virus-related Web searches.

METHODS

Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis.

RESULTS

In the regression analysis, an association between Web searches and "real-world" epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus-related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus-related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus-related Wikipedia pages.

CONCLUSIONS

Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.

Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization

Syndromic Surveillance aims at analyzing medical data to detect clusters of illness or forecast disease outbreaks. Although the research in this field is flourishing in terms of publications, an insight of the global research output has been overlooked. This paper aims at analyzing the global scientific output of the research from 1993 to 2017. To this end, the paper uses bibliometric analysis and visualization to achieve its goal. Particularly, a data processing framework was proposed based on citation datasets collected from Scopus and Clarivate Analytics’ Web of Science Core Collection (WoSCC). The bibliometric method and Citespace were used to analyze the institutions, countries, and research areas as well as the current hotspots and trends. The preprocessed dataset includes 14,680 citation records. The analysis uncovered USA, England, Canada, France and Australia as the top five most productive countries publishing about Syndromic Surveillance. On the other hand, at the Pinnacle of academic institutions are the US Centers for Disease Control and Prevention (CDC). The reference co-citation analysis uncovered the common research venues and further analysis of the keyword cooccurrence revealed the most trending topics. The findings of this research will help in enriching the field with a comprehensive view of the status and future trends of the research on Syndromic Surveillance.

The Case for Computational Health Science

In this introductory paper, we begin by making the case for Computational Health Science, which we define as the interdisciplinary efforts of health scientists, computer scientists, engineers, psychologists, and other social scientists, to conduct innovative research that will inform future practice directed at changing health behavior through improved communication, networking, and social capital. We recognize and discuss some of the main challenges involved with such an enterprise, but also highlight the associated benefits, which, we argue, significantly outweigh them. We then provide a brief summary of the contributions to this first Special Issue on Computational Health Science.

Trends of infodemiology studies: a scoping review

INTRODUCTION

The health care industry is rich in data and information. Web technologies, such as search engines and social media, have provided an opportunity for the management of user generated data in real time in the form of infodemiology studies. The aim of this study was to investigate infodemiology studies conducted during 2002-2016, and compare them based on developed, developing and in transition countries.

METHODS

This scoping review was conducted in 2017 with the help of the PRISMA guidelines. PubMed, Scopus, Science Direct, Web of Knowledge, Google Scholar, Wiley and Springer databases were searched between the years 2002 and 2016. Finally, 56 articles were included in the review and analysed.

RESULTS

The initial infodemiology studies pertain to the quality assessment of the hospital's websites. Most of the studies were on developed countries, based on flu, and published in the Journal of Medical Internet Research.

CONCLUSION

The infodemiology approach provides unmatched opportunities for the management of health data and information generated by the users. Using this potential will provide unique opportunities for the health information need assessment in real time by health librarians and thereby provide evidence based health information to the people.

An infodemiology study on breast cancer in Iran

Purpose

According to the studies conducted in Iran, the breast cancer is the most frequent type of cancer among women. This study aimed to explore the state of health information supply and demand on breast cancer among Iranian medical researchers and Iranian Web users from 2011 to 2015.

Design/methodology/approach

A mixed method research is conducted in this study. In qualitative part, a focus group interview is applied to the users to identify their selected keywords searched for breast cancer in Google. The collected data are analyzed using Open Code software. In quantitative part, data are synthesized using the R software in two parts. First, users’ internet information-seeking behavior (ISB) is analyzed using the Google Trends outputs from 2011 to 2015. Second, the scientific publication behavior of Iranian breast cancer specialists are surveyed using PubMed during the period of the study.

Findings

The results show that the search volume index of preferred keywords on breast cancer has increased from 4,119 in 2011 to 4,772 in 2015. Also, the findings reveal that Iranian scholars had 873 scientific papers on breast cancer in PubMed from 2011 to 2015. There was a significant and positive relationship between Iranian ISB in the Google Trends and SPB of Iranian scholars on breast cancer in PubMed.

Research limitations/implications

This study investigates only the state of health information supply and demand in PubMed and Google Trends and not additional databases often used for medical studies and treatment.

Originality/value

This study provides a road map for health policymakers in Iran to direct the breast cancer studies.

Vesicular stomatitis forecasting based on Google Trends

Background

Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends.

Methods

American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression.

Results

For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively.

Conclusion

This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast.

Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends

Background

Mayaro virus (MAYV), first discovered in Trinidad in 1954, is spread by the Haemagogus mosquito. Small outbreaks have been described in the past in the Amazon jungles of Brazil and other parts of South America. Recently, a case was reported in rural Haiti.

Objective

Given the emerging importance of MAYV, we aimed to explore the feasibility of exploiting a Web-based tool for monitoring and tracking MAYV cases.

Methods

Google Trends is an online tracking system. A Google-based approach is particularly useful to monitor especially infectious diseases epidemics. We searched Google Trends from its inception (from January 2004 through to May 2017) for MAYV-related Web searches worldwide.

Results

We noted a burst in search volumes in the period from July 2016 (relative search volume [RSV]=13%) to December 2016 (RSV=18%), with a peak in September 2016 (RSV=100%). Before this burst, the average search activity related to MAYV was very low (median 1%). MAYV-related queries were concentrated in the Caribbean. Scientific interest from the research community and media coverage affected digital seeking behavior.

Conclusions

MAYV has always circulated in South America. Its recent appearance in the Caribbean has been a source of concern, which resulted in a burst of Internet queries. While Google Trends cannot be used to perform real-time epidemiological surveillance of MAYV, it can be exploited to capture the public’s reaction to outbreaks. Public health workers should be aware of this, in that information and communication technologies could be used to communicate with users, reassure them about their concerns, and to empower them in making decisions affecting their health.