Google Search Queries Research Articles

Search for a New Home: Refugee Stock and Google Search

Following the assumption that trends of online queries may indicate intentions and help to predict human behavior, this study addresses the general issue of analyzing, nowcasting, and predicting migrant decisions through an analysis of Google search patterns in the case of Syrians in Turkey. Aiming to contribute to the literature on predicting migration patterns, we examine the relationship between Google search queries for province names in Turkey and the number of Syrians under temporary protection across provinces from January 2016 to December 2019 and demonstrate a positive and significant association. Then, we explore the predictive power of Google searches in predicting the stock of Syrians under temporary protection in Turkey across provinces. We exploit the alphabetical difference between Turkish and Arabic as the method of differentiation between host and migrant populations. Our findings indicate that Google searches can be good predictors for estimating refugee stocks, especially when traditional data are not available. They can also be helpful in forecasting the changing pattern of migrant stocks at frequent intervals, to which conventional socioeconomic indicators are less sensitive due to their less frequent reporting periods.

Assessing the readability and quality of online patient information for laser tattoo removal

Just as tattoos continue to increase in popularity, many people with tattoos also seek removal, often due to career concerns. Prospective clients interested in laser tattoo removal may do research about the procedure online, as the internet increasingly becomes a resource to get preliminary health information. However, it is important that the online health information on the topic be of high quality and be accessible to all patients. We analyzed 77 websites from a Google search query using the terms “Laser tattoo removal patient Information” and “Laser tattoo removal patient Instructions” to assess this. The websites were evaluated for their readability using multiple validated indices and comprehensiveness. We found that websites had a broad readability range, from elementary to college, though most were above the recommended eighth-grade reading level. Less than half of the websites adequately discussed the increased risk of pigmentary complications in the skin of color clients or emphasized the importance of consulting with a board-certified dermatologist/plastic surgeon before the procedure. Over 90% of the websites noted that multiple laser treatments are likely needed for complete clearance of tattoos. The findings from our study underscore a significant gap in the accessibility and quality of online information for patients considering laser tattoo removal, particularly in addressing specific risks for patients with darker skin tones and emphasizing the need for consulting a board-certified physician before undergoing the procedure. It is important that online resources for laser tattoo removal be appropriately written to allow better decision-making, expectations, and future satisfaction for potential clients interested in the procedure.

Does digitalisation affect the adoption of electric vehicles? New regional-level evidence from Google Trends data

ABSTRACT Digitalisation is an important dimension that contributes to fostering the adoption of electric mobility. We investigate this unexplored topic by focusing on the regional level of analysis, and presenting new data and evidence for a large number of regions in Europe, Canada and the United States. The empirical analysis makes use of Google Trends data. It constructs new indicators of digitalisation and the adoption of electric vehicles, as measured by Google search queries. The new dataset contains indicators for 182 regions in 15 countries for the period 2010–23. We use this dataset to carry out a time-series analysis (vector error correction (VEC) model) of the relations between digitalisation and electric vehicles in each region. The results show that digitalisation is an important factor that has fostered the adoption of electric vehicles in the last decade. The analysis, though, also points out that there is considerable heterogeneity in the time-series results among regions in our sample. Digitalisation has a more visible effect on electric mobility for regions that have higher gross domestic product per capita, better internet infrastructures, a young and well-educated workforce, and higher population density.

State Cannabis and Psychedelic Legislation and Microdosing Interest in the US

ImportanceDespite growing interest in psychedelics, there is a lack of routine population-based surveillance of psychedelic microdosing (taking “subperceptual” doses of psychedelics, approximately one-twentieth to one-fifth of a full dose, over prolonged periods). Analyzing Google search queries can provide insights into public interest and help address this gap.ObjectiveTo analyze trends in public interest in microdosing in the US through Google search queries and assess their association with cannabis and psychedelic legislative reforms.Design, Setting, and ParticipantsIn this cross-sectional study, a dynamic event-time difference-in-difference time series analysis was used to assess the impact of cannabis and psychedelic legislation on microdosing search rates from January 1, 2010, to December 31, 2023. Google search rates mentioning “microdosing,” “micro dosing,” “microdose,” or “micro dose” within the US and across US states were measured in aggregate.ExposureEnactment of (1) local psychedelic decriminalization laws; (2) legalization of psychedelic-assisted therapy and statewide psychedelic decriminalization; (3) statewide medical cannabis use laws; (4) statewide recreational cannabis use laws; and (5) all cannabis and psychedelic use restricted.Main Outcome and MeasuresMicrodosing searches per 10 million Google queries were measured, examining annual and monthly changes in search rates across the US, including frequency and nature of related searches.ResultsSearches for microdosing in the US remained stable until 2014, then increased annually thereafter, with a cumulative increase by a factor of 13.4 from 2015 to 2023 (7.9 per 10 million to 105.6 per 10 million searches, respectively). In 2023, there were 3.0 million microdosing searches in the US. Analysis at the state level revealed that local psychedelic decriminalization laws were associated with an increase in search rates by 22.4 per 10 million (95% CI, 7.5-37.2), statewide psychedelic therapeutic legalization and decriminalization were associated with an increase in search rates by 28.9 per 10 million (95% CI, 16.5-41.2), statewide recreational cannabis laws were associated with an increase in search rates by 40.9 per 10 million (95% CI, 28.6-53.3), and statewide medical cannabis laws were associated with an increase in search rates by 11.5 per 10 million (95% CI, 6.0-16.9). From August through December 2023, 27.0% of the variation in monthly microdosing search rates between states was explained by differences in cannabis and psychedelics legal status.Conclusion and RelevanceThis cross-sectional study found that state-led legislative reforms on cannabis and psychedelics were associated with increased public interest in microdosing psychedelics.

Smart technologies in banking

The article is aimed at the current issues of using smart technologies and innovative approach during evolution and transformation processes in banking. The study identifies the special place of this topic for achieving a high level of efficiency and competitiveness of banks and characterizes the impact of the introduction of technological approaches on the customer base and its perception of banking products. The main functions of banking innovations in this area are analyzed and the justification of their feasibility at the present stage of economic development is provided. A number of the most promising technologies and approaches to banking activities are allocated, namely: contactless payment, digital wallets, biometric identification, person-to-person payments, collective financing, omnichannel banking, interaction with FinTech companies, blockchain, big data, artificial intelligence, smart machines, Internet of Things, behavioral banking, retail bank, application programming interfaces, multi-component bank, open banking, augmented reality, robotic automation, hybrid clouds. The relevance of the identified areas is proved based on their perception by analyzing the popularity of the identified topics in Google search queries using the Google Trends tool. The perception of smart technologies in banking by Internet users in the world and specifically in Ukraine is investigated, which gave grounds to conclude that there is a significant interest in them, and therefore the expediency of further study and implementation in the activities of banks. It is identified that the most perspective technologies are biometric identification, blockchain, Internet of Things, big data analysis, artificial intelligence, etc. Several technologies have been identified, namely, collective financing (crowdfunding), application programming interfaces (APIs) and digital wallets, which are less popular in Ukraine than in the world in general, and therefore require detailed research and study of the relevance of their application in the domestic banking market. Possible directions for further innovative development of banking institutions based on the use of smart technologies are proposed. Based on panel data for 60 banks of Ukraine for the period 2014-2022, the author analyzes the correlations between the indicators of the use of digital technologies and the financial performance of banks and builds regression dependencies of financial indicators of banks on the indicator of the number of electronic means of payment in active circulation. The theoretical value of the study is to identify the most promising smart technologies and innovative approaches to banking business in modern conditions. The practical value lies in studying the level of perception of high-tech innovations in the field of banking services by the active public and identifying further directions for the development of this process. We consider it advisable to direct further research in the context of a detailed study of the possibilities of applying the identified technologies in specific banking products or business processes.

The Alarm Clock Against the Sun: Trends in Google Trends Search Activity Across the Transitions to and from Daylight Saving Time

The human circadian timing system depends on the light/dark cycle as its main cue to synchronize with the environment, and thus with solar time. However, human activities depend also on social time, i.e. the set of time conventions and restrictions dictated by society, including Daylight Saving Time (DST), which adds an hour to any degree of desynchrony between social and solar time. Here, we used Google Trends as a data source to analyze diurnal variation, if any, and the daily peak in the relative search volume of 26 Google search queries in relation to the transitions to/from DST in Italy from 2015 to 2020. Our search queries of interest fell into three categories: sleep/health-related, medication and random non sleep/health-related. After initial rhythm and phase analysis, 11 words were selected to compare the average phase of the 15 days before and after the transition to/from DST. We observed an average phase advance after the transition to DST, and a phase delay after the transition to civil time, ranging from 25 to 60 minutes. Advances or delays shorter than 60 minutes, which were primarily observed in the sleep/health-related category, may suggest that search timing for these queries is at least partially driven by the endogenous circadian rhythm. Finally, a significant trend in phase anticipation over the years was observed for virtually all words. This is most likely related to an increase in age, and thus in earlier chronotypes, amongst Google users.

Google Search Analytics for Lateral Epicondylitis.

The use of online search engines for health information is becoming common practice. We analyzed Google search queries to identify the most frequently asked topics and questions related to lateral epicondylitis ("tennis elbow") and the Web sites provided to address these questions. Four search terms for lateral epicondylitis were entered into Google Web Search. A list of the most frequently asked questions along with their associated Web sites was extracted and categorized by 2 independent reviewers. A total of 400 questions were extracted with 168 associated Web sites. The most popular question topics were related to indications/management (39.0%), risks/complications (19.5%), and the ability to perform specific activities (18.8%). Frequently asked questions had to do with the duration of symptoms, self-management strategies (eg, brace use and self-massage), and the indications for surgery. The most common Web sites provided to address these questions were social media (27.5%), commercial (24.5%), academic (16.5%), and medical practice (16.3%). The most frequently asked questions about lateral epicondylitis on Google centered around symptom duration and management, with most information originating from social media and commercial Web sites. Our data can be used to anticipate patient concerns and set expectations regarding the prognosis and management of lateral epicondylitis.

Potential Determinants Contributing to COVID-19 Vaccine Acceptance and Hesitancy in Taiwan: Rapid Qualitative Mixed Methods Study.

Although vaccination has been shown to be one of the most important interventions, COVID-19 vaccine hesitancy remains one of the top 10 global public health challenges worldwide. The objective of this study is to investigate (1) major determinants of vaccine hesitancy, (2) changes in the determinants of vaccine hesitancy at different time periods, and (3) the potential factors affecting vaccine acceptance. This study applied a mixed methods approach to explore the potential determinants contributing to vaccine hesitancy among the Taiwanese population. The quantitative design of this study involved using Google Trends search query data. We chose the search term "" (vaccine), selected "" (Taiwan) as the location, and selected the period between December 18, 2020, and July 31, 2021. The rising keywords related to vaccine acceptance and hesitancy were collected. Based on the responses obtained from the qualitative study and the rising keywords obtained in Google Trends, the 3 most popular keywords related to vaccine hesitancy were identified and used as search queries in Google Trends between December 18, 2020, and July 31, 2021, to generate relative search volumes (RSVs). Lastly, autoregressive integrated moving average modeling was used to forecast the RSVs for the 3 keywords between May 29 and July 31, 2021. The estimated RSVs were compared to the observed RSVs in Google Trends within the same time frame. The 4 prevailing factors responsible for COVID-19 vaccine acceptance and hesitancy were doubts about the government and manufacturers, side effects, deaths associated with vaccination, and efficacy of vaccination. During the vaccine observation period, "political role" was the overarching consideration leading to vaccine hesitancy. During the peak of the pandemic, side effects, death, and vaccine protection were the main factors contributing to vaccine hesitancy. The popularity of the 3 frequently searched keywords "side effects," "vaccine associated deaths," and "vaccine protection" continued to rise throughout the pandemic outbreak. Lastly, the highest Google search queries related to COVID-19 vaccines emerged as "side effects" prior to vaccination, deaths associated with vaccines during the period when single vaccines were available, and "side effects" and "vaccine protection" during the period when multiple vaccines were available. Investigating the key factors influencing COVID-19 vaccine hesitancy appears to be a fundamental task that needs to be undertaken to ensure effective implementation of COVID-19 vaccination. Google Trends may be used as a complementary infoveillance tool by government agencies for future vaccine policy implementation and communication.

Herding and Google search queries in the Brazilian stock market

PurposeThis study investigates the presence of herding in the Brazilian stock market between 2012 and 2020 and associates it with the volume of searches on the Google platform.Design/methodology/approachFollowing methodologies are used to investigate the presence of herding: the Cross-Sectional Standard Deviation of Returns (CSSD), the Cross-Sectional Absolute Deviation (CSAD) and the Cross-Sectional Deviation of Asset Betas to the Market.FindingsMost of the models detected herding. In addition, there was a causal relationship between peaks in Google search volumes and the incidence of herding across the whole period, especially in 2015 and 2019.Originality/valueThis study suggests that confirmation bias influences investors' decisions to buy or sell assets.

United States of Mind under Uncertainty

This paper investigates if heightened economic uncertainty raises concerns about mental health in the U.S. We first quantify such concerns by constructing a composite Mental Health Concerns index, using time series of the intensity of Google search queries related to mental disorders and distress. This index i) rises significantly during the three recessionary episodes and ii) comoves negatively with survey responses that reflect views on current consumer sentiment or on future economic conditions. We find that the concerns regarding mental health substantially increase after an unexpected hike in economic uncertainty; uncertainty not only channels through its negative impacts on economic activity, but also directly affects the level of concerns. Our findings suggest that an uncertainty shock can have a far-reaching impact on overall welfare of economic agents by leaving them more concerned about mental health.

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

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

Deep Learning Models for Airport Demand Forecasting With Google Trends

Managers gain new insights into how operational benefits can be achieved. Forecasting problems for passenger flow in airports are gaining interest among marketing researchers, but comparison of stochastic optimisation methods via deep learning forecasts with search query data is not yet available in the aviation field. To fill this gap, the current study predicts the demand of Madrid airport demand with Google search query data using H2O deep learning method. The findings indicate that there is a long-term relationship between search queries and actual passenger demand. Besides, search queries “fly to madrid,” and “flights to madrid spain” were found to be the cause of the actual domestic air passenger demand in Madrid. Also, to determine the best forecasting accuracy, stochastic gradient descent (SGD) optimisers were used. Specifically, findings indicate that Adam is a better optimiser increasing forecasting accuracy for Madrid airports.

Regional Patterns Of Third-Level Digital Inequality In Russia: An Analysis Of Google Trends Data

Digital inequality extends beyond mere access to technology. This study explores the concept of third-level digital inequality, which describes the situation where individuals or communities have access to technology and the Internet, have required skills, but still struggle to use it effectively. However, there is currently a lack of data and methods for assessing thirdlevel digital inequality. To address this gap, this study aimed to evaluate it on a regional scale by analyzing the popularity of Google search queries. In proposed method, the data are categorized into three groups: everyday services, education, science, and technology, and entertainment. On this basis authors calculated the index of Internet usage efficiency. The study’s findings revealed the territorial patterns of digital inequality in the constituent entities of the Russian Federation. Regions in North Caucasus and Siberia showed low Internet usage efficiency, while regions in the Urals and Central Russia had high Internet usage efficiency. The study’s methodology is quick, cost-effective, and easy to implement, but it also has limitations. The method only considers the popularity of certain search queries and does not consider the frequency or duration of internet usage, or the specific websites or services accessed, and does not consider individual-level factors that may influence internet usage patterns. The authors emphasize the importance of addressing not only differences in Internet access but also the lack of technology skills, digital literacy, and motivation among certain groups. They conclude that public policies aimed at enhancing internet skills can reduce digital inequality and improve the quality of life of the population.

Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks

We analyze the ability of an index of mortgage default risks (MDRI) for 43 states and 20 metropolitan statistical areas (MSA) of the US derived from Google search queries, in predicting (in- and out-of-sample) housing returns of the corresponding states and MSAs, based on various panel data and time-series approaches. In general, our results tend to prefer the panel data model based on common correlated effects estimation. We highlight that growth in MDRI negatively impacts housing returns within-sample, with predictive gains primarily concentrated beyond a year. These results are robust to alternative out-of-sample periods and econometric frameworks. Given the role of house prices as a leading indicators, our results are of value to policymakers, especially at the longer-run.

Evaluating the Impact of Mask Mandates and Political Party Affiliation on Mental Health Internet Search Behavior in the United States During the COVID-19 Pandemic: Generalized Additive Mixed Model Framework.

The impacts of the COVID-19 pandemic on mental health worldwide and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates and political party affiliation. The goal of this study was to examine whether statewide mask mandates and political party affiliations yielded differential changes in mental health symptoms across the United States by leveraging state-specific internet search query data. This study leveraged Google search queries from March 24, 2020, to March 29, 2021, in each of the 50 states in the United States. Of the 50 states, 39 implemented statewide mask mandates-with 16 of these states being Republican-to combat the spread of COVID-19. This study investigated whether mask mandates were associated differentially with mental health in states with and without mandates by exploring variations in mental health search queries across the United States. In addition, political party affiliation was examined as a potential covariate to determine whether mask mandates had differential associations with mental health in Republican and Democratic states. Generalized additive mixed models were implemented to model associations among mask mandates, political party affiliation, and mental health search volume for up to 7 months following the implementation of a mask mandate. The results of generalized additive mixed models revealed that search volume for "restless" significantly increased following a mask mandate across all states, whereas the search volume for "irritable" and "anxiety" increased and decreased, respectively, following a mandate for Republican states in comparison with Democratic states. Most mental health search terms did not exhibit significant changes in search volume in relation to mask mandate implementation. These findings suggest that mask mandates were associated nonlinearly with significant changes in mental health search behavior, with the most notable associations occurring in anxiety-related search terms. Therefore, policy makers should consider monitoring and providing additional support for these mental health symptoms following the implementation of public health-related mandates such as mask mandates. Nevertheless, these results do not provide evidence for an overwhelming impact of mask mandates on population-level mental health in the United States.

COVID-19 Related Early Google Search Behavior and Health Communication in the United States: Panel Data Analysis on Health Measures.

The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.

Estimation of Lockdowns' Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data.

Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.

Analysis of the Relationship Between COVID-19 Infections and Web-Based Housing Searches

Abstract The study used Google search query data on real estate interest for several countries in the Baltic area. The dynamics of public interest in housing have been compared to the dynamics of the COVID-19 infections in Lithuania, Latvia, Poland, and Sweden. This study uses the Vector autoregressive (VAR) model to forecast such time series. VAR is a multivariate linear time series model in which the endogenous variables in the system are lagged functions of the values of all endogenous variables. The increase in COVID-19 infections negatively affected society’s interest in housing. The study used Google Trends and R software.

Impact of the Food and Drug Administration enforcement policy on flavored e-cigarettes on the online popularity of disposable e-cigarettes: analyses of Google search query data

BackgroundThe impact of the U.S. Food and Drug Administration's (FDA) initial enforcement policy on flavored cartridge-based e-cigarettes and subsequent notice for the removal of flavored disposable electronic cigarettes (e-cigarettes) such as Puff Bar from the market has not been well evaluated. We, therefore, sought to examine the impact of the e-cigarette flavor-related policy changes on the online popularity of Puff Bar, a prototypic disposable e-cigarette.MethodsWe tabulated the total weekly Google search queries originating from the U.S. for "Puff Bar" and "Puff Bars" from January 1, 2019, to December 31, 2021. We divided the three years into four (4) distinct periods using the dates of the initial announcement to ban non-tobacco flavored e-cigarettes (September 11, 2019), the finalization of the FDA enforcement policy on cartridge-based flavors (January 2, 2020), and the notice for the market withdrawal of flavored disposable e-cigarettes (July 20, 2020) as reference time points. Then, we used piecewise linear regression and autoregressive integrated moving average (ARIMA) to compare the trends in searches for Puff Bar for the four (4) periods.ResultsBefore the initial announcement to ban non-tobacco flavored e-cigarettes, online search queries (per 10 million Google searches) for Puff Bar were slowly rising at a rate of 0.58 queries per week (95%CI: -0.80 – 1.97). Following the announcement, searches for Puff Bar increased significantly at a rate of 16.61 queries per week (95%CI: 12.13 – 21.10). The rate of searches for Puff Bar then increased exponentially at 40.08 queries per week (95%CI: 27.32 – 52.84) following the FDA flavor ban, which excluded disposable e-cigarettes. Then, the rate of increase declined but remained relatively stable at 3.67 queries per week (95%CI: 0.69–6.65) until the FDA's notice to remove flavored Puff Bar products from the market. Following this notice, the rate of searches for Puff Bar significantly declined (rate: -4.97 queries per week; 95%CI: -5.40—-4.54).ConclusionsThe tracking of online search data demonstrates rapid public recognition of the FDA's announcements of tobacco regulatory actions.

Using “Google trends” for dengue surveillance and epidemiological research

Millions of people worldwide search online for health-related information and search engines have become an increasingly popular resource for accessing health-related information and provides valuable source. Key words used as well as the number and geographic location of searches can provide trend data, available by Google trends. In this study exploring this resource using dengue disease as an example. Objectives were to use Google trends data for comparison across different locations in India for the past 5 years, and to assess the specific search terms used in Google trends data and to correlate the real time dengue outbreak of Tamil Nadu with Google trend search. It was a cross sectional study. Data collection was done via Google search queries and record was included. Weekly trends were accessed from Google Trends. Data is a randomly collected sample of real time and non-real time Google search queries. Search traffic for the string “dengue fever” reflected increased likelihood of exposure and the string “dengue symptoms and treatment” had higher relative traffic during rainy season. Cities and states with the highest amount of search traffic for “dengue disease” overlapped where dengue is endemic. Found that search trend data produced by Google to approximate the seasonality, spikes at September to November every year and geographic distribution also identified in dengue disease. Web search query data were found to be capable of tracking dengue activity and predict periods of large incidence of dengue with high accuracy and may prove useful.