cord-018632-azrqz6hf	2019	Artificial Intelligence (AI) offers new hope in not only effectively pre-empting, preventing and combating the threats of infectious disease epidemics, but also facilitating the understanding of health-seeking behaviors and public emotions during epidemics. The human population is currently able to access potentially useful massive data sources of infectious disease spread through sentinel reporting systems, national surveillance systems (usually operated by national or regional disease centers such as the Center for Disease Control (CDC)), genome databases, internet search queries (also called infodemiology and infoveillance studies) [10] [11] [12] , Twitter data analysis [13, 14] , outbreak investigation reports, transportation dynamics [15] , vaccine reports [16] and human dynamics information [17] . With such high fluxes of health-seeking behavior using computers, a group of Italian researchers'' evaluated Google Trends search queries for terms related to "Ebola" outbreak at the global level and across countries where primary cases of Ebola were reported [26] .
cord-018688-gvk9uazp	2018	We examined the source of information, the manner in which they process and disseminate the information, their role in each phase of disease outbreaks, and whether and to what extent these systems are capable of early detection and management of infectious disease epidemics. Conclusions Currently, there is little prospective evidence that existing informal systems are capable of real-time early detection of disease outbreaks. The systems evaluated were ProMED-mail, Global Public Health Intelligence Network (GPHIN), HealthMap, MediSys, EpiSPIDER, BioCaster, H5N1 Google Earth mashup, Avian Influenza Daily Digest and Blog, Google flu trends and Argus. The aim is to enhance the surveillance of infectious disease outbreaks.EpiSPIDER uses ProMED-mail reports as an input, as well as health news sources that provide RSS feeds. Another retrospective study tested the real-time detection ability of six informal digital systems, including Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail.
cord-021088-9u3kn9ge	2015	Instead, today''s successful big data business models largely use data to scale old modes of value creation, rather than invent new ones altogether. Four of these assumptions merit special attention: First, N = all, or the claim that our data allow a clear and unbiased study of humanity; second, that today = tomorrow, or the claim that understanding online behavior today implies that we will still understand it tomorrow; third, offline = online, the claim that understanding online behavior offers a window into economic and social phenomena in the physical world; and fourth, that complex patterns of social behavior, once understood, will remain stable enough to become the basis of new data-driven, predictive products and services in sectors well beyond social and media markets. The rate of change in online commerce, social media, search, and other services undermines any claim that we can actually know that our N = all sample that works today will work tomorrow.
cord-120442-qfgoue67	2020	title: The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study The goal of this study is to examine, among college students, the relationship between deteriorating mental health conditions and changes in user behaviors when engaging with Google Search and YouTube during COVID-19. In this study, we collected longitudinal individual-level Google Search and YouTube data from college students, and we measured their anxiety (GAD-7) and depression (PHQ-9) levels before and after the outbreak of COVID-19. First, while most of the online behavioral features we developed showed significant differences between groups of students with and without deteriorating anxiety and depressive disorders during COVID-19, our study cohort only represented a small portion of the whole population suffering from mental health difficulties.
cord-193136-7g6qr73e	2020	(2018) "Google Trends shows the changes in online interest for time series in any selected term in any country or region over a selected time period, for example, a specific year, several years, 3 weeks, 4 months, 30 days, 7 days, 4 hours, 1 hour, or a specified time-frame." They argue that as the internet penetration is increasing web based search activity has become a valid indicator of public behaviour. The paper positions itself in this direction; applying various tools and techniques of scientometrics, Altmetrics and Google Trends to draw meaning from the huge volume of research papers and online activity surrounding this pandemic. The trends observed in measures like lockdown, social distancing and quarantine at global and country level showed the societal increasing concern with these aspects.The findings of this study suggests how the research and public interest has been shaped around this disease.
cord-232959-jcnvnn2k	2020	To partially overcome these issues, in this work we investigate the relation between the different private data sources, and how can they be used complementary to provide a better understanding of mobility. This includes a general study of mobility trends for all regions and data sources ( §4.1), a discussion on the anomalies observed ( §4.2), an analysis on the daily trends ( §4.3) and some insights on the new normality ( §4.4). The second one, movement between tiles, estimates mobility by computing how many different tiles are visited by the sample of people, compared with the same number during the same day of the week previous to the pandemics (February 2020) [12] . In this work we consider the use of private data sources (Google and Facebook) for assessing the levels of mobility in a country like Spain. Regarding private data sources, we have shown the differences between using an absolute measure (like Facebook) and a relative measure (like Google).
cord-252218-jrgl0x06	2020	8 We retrieved worldwide public query data for the following terms: ''quit smoking'', ''smoking cessation'', ''help quit smoking'' and ''nicotine gum'' between 9 January 2020 and 6 April 2020. The Google Trends data for Web search queries for the terms ''smoking cessation'' and ''nicotine gum'' from 9 January 2020 to 6 April 2020 are shown in Fig. 1 . Previous Google Trends studies have found increased numbers of seaches relating to smoking cessation in association with the launch of national smoking cessation programmes and changes in tobacco control policies. 10 We found no increase in the number of searches for smoking cessation on Google in the first months of the COVID-19 pandemic. Smoking cessation campaigns are important as smokers are more vulnable to viral infections and lung diseases, and appear to have worse outcomes when hospitalised with COVID-19 than non-smokers. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
cord-256094-f85xc5uu	2014	This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases. This study, however, did not aim to develop actionable surveillance systems, produce predictive models of infectious disease based on internet-based data or to identify the best search terms for use in these models. Briefly, the time series analysed were monthly case numbers for the 64 infectious diseases monitored by the Australian Government''s National Notifiable Disease Surveillance System (NNDSS) and Google Trends monthly search metrics for related internet search terms. To our knowledge, assessments of the use of internet-based surveillance have only been performed for five of the 17 diseases that were demonstrated to have a significant association with internet search terms (influenza [4] , dengue [9, 27] , chickenpox [11, 12] , hepatitis B [14] and cryptosporidiosis [13] the authors of the final study were, however, not able to detect signals from internet search queries).
cord-262310-z0m6uuzf	2020	Methods We performed a Google TrendsTM search for "Coronavirus" and compared Relative Search Volumes (RSV) indices to the number of reported COVID-19 cases by the European Center for Disease Control (ECDC) using time-lag correlation analysis. The worldwide Google TrendsTM index reached its peak on the 12th of March 2020 at a time when numbers of infected patients started to increase in Europe and COVID-19 was declared a pandemic. In European countries, especially in Italy, a small peak in the Google Trends TM analysis was found during the outbreak in China and a climax was found on February 23 rd 2020, a few days before the numbers of newly COVID-19 started to increase exponentially. The peak of search queries was March 3 rd a new increase in RSV is found in Brazil, followed by increasing numbers of newly confirmed cases of COVID-19 ( Figure 2 ).
cord-265178-q7x7ec24	2020	We show that during this period, fear of the coronavirus – manifested as excess search volume – represents a timely and valuable data source for forecasting stock price variation around the world. The idea of using sentiment or fear to explain stock market volatility is certainly not new; several recent studies have used news, VIX, Twitter posts and other proxies to measure investors'' sentiment and fear about the future (e.g., Whaley, 2000; Zhang et al., 2011; Huerta et al., 2011; Smales, 2014 Smales, , 2017 . However, our study is the first to address the predictive power of Google searches on stock market volatility during the COVID-19 pandemic. Our results show that high Google search volumes 35 for COVID-19 predict high stock market volatility in all markets in our sample. The ASV A t is positive for all markets and significant for all markets except South Korea, thus suggesting that when search activity related to corona information increased, price variation in stock markets increased the following day.
cord-289647-14ba5sro	2020	OBJECTIVE: To determine the relative correlations of Twitter and Google Search user trends concerning smell loss with daily coronavirus disease 2019 (COVID-19) incidence in the United States, compared to other severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) symptoms. 5 As such, although significant correlations between Google searches pertaining to anosmia and COVID-19 incidence have already been reported, our intention in the present study is to better understand the relative value of alternative infodemiological parameters (nonsmell symptoms, COVID-19 searches and tweets) and platforms (Twitter) in estimating COVID-19 infection trajectory in the United States. Table SA in the online version of the article); data pertaining to March 22, 2020, and the 2 following days were excluded in 1 iteration of the analysis to help evaluate quantitatively the effect of discrete, lay media transmissions on Twitter and Google search trend correlations with COVID-19 incidence.
cord-294955-bybdn9yb	2020	OBJECTIVE: To assess whether web-based public inquiries into pharyngitis-related search terms follow annual incidence peaks of acute pharyngitis in various countries from both hemispheres. Considering that the vast majority of teenagers and adults use the World Wide Web to acquire health-related information, we hypothesized that peaks in web-based internet searches for pharyngitis-related symptoms might also follow the global incidence rates of this condition. Therefore, the aim of this study was to assess web-based public interest for acute pharyngitis and related-terms for seasonal variations globally. To assess and illustratively depict seasonal variations of global RSV for pharyngitis-related search terms, we included countries from both hemispheres. The current study revealed winter peaks in World Wide Web inquiries for pharyngitis-related terms in countries from both hemispheres. Therefore, it is crucial to provide reliable, easily accessible, and publicly available online information on diagnosis, treatment, and red-flag symptoms of usually self-limiting medical conditions such as acute pharyngitis.
cord-296821-qdhj9zj6	2020	This study aims to investigate the interest in quitting smoking and alcohol during the lockdown period in India since March 25 to know the effectiveness of public awareness measures conducted regarding the negative aspects of smoking and alcohol during COVID-19 pandemic. Our study results showed no consistent increase in the number of searches for quitting smoking or quitting alcohol on Google during the study period (February to May). A recent study analysing the Google trend regarding smoking cessation searches worldwide during the early months of the COVID-19 outbreak (9 January 2020 and 6 April 2020) also failed to show a tendency for increased interest in any of the key terms related to smoking cessation (''quit smoking'', ''smoking cessation'', ''help quit smoking'' and ''nicotine gum'') [8] . Our study results may indicate that there has been no significant increased interest in quitting smoking and alcohol, at least among the Indian population who use online resources for health-related information.
cord-297835-ukrz8tlv	2020	title: Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a light-rail tram Contact tracing apps based on the Google/Apple Exposure Notification (GAEN) API [4] are currently being rolled out across Europe, with apps already deployed in Italy, Switzerland and Germany. We observe that changing the people holding a pair of handsets, with the location of the handsets otherwise remaining unchanged, can cause variations of ±10dB in the attenuation level reported by the GAEN API. To provide baseline data on the radio propagation environment we also used the standard Android Bluetooth LE scanner API to collect measurements of RSSI as the distance was varied between two Google Pixel 2 handsets placed at a height of approximately 0.5m (about the same height as the tram seating) in the centre aisle of the tram carriage.
cord-298953-9aifql2f	2020	The second key resource used in this paper is the Outdoor Recreation Valuation (ORVal) model (Day and Smith 2017) , which we use not only to predict demand for visits to greenspace under the restrictive rules of the lockdown but also to estimate the changes in economic value experienced by residents of England as a consequence of those rules. In this paper, we assume that differences between the ORVal predictions of recreation behaviour under the lockdown rules and those observed in the Google mobility data are the net result of those, and possibly other, factors. 8 Given the nature of the MENE data, the ORVal model progresses from the assumption that each day represents a recreation choice occasion on which individuals can select from a choice set comprising (1) not taking an outdoor trip, and then (2) an option for traveling to each site by car and (3) an option for each site visited on foot.
cord-302758-i5pe61h1	2020	OBJECTIVE: To assess trends of Google Search queries for symptoms and complaints encountered commonly in otolaryngology practices during the coronavirus disease 2019 (COVID-19) pandemic when in-person care has been limited. CONCLUSION: This study demonstrates that Google search activity for many otolaryngology-related terms during the COVID-19 pandemic has increased or decreased significantly as compared to previous years. This study aims to assess trends within the U.S. for Google Search queries of symptoms and complaints encountered commonly in otolaryngology practices comparing the time of COVID-19 pandemic with similar time periods in previous years. The COVID-19 pandemic has challenged the ability of otolaryngologists to provide care to many patients in the U.S. This study demonstrates that Google search activity for many otolaryngology-related terms during this period has increased or decreased significantly as compared to previous years.
cord-304183-zv3s7cjq	2020	title: Evaluating the mainstream impact of ophthalmological research with Google Trends A scatter plot relating the two variables ( Fig. 2 ) indicated weak positive correlation overall, with most points lying outside the 95% confidence intervals of the best-fit trend-line. The overall correlation between Google interest and PubMed publications indicates concordance between the interests of the scientific community and general public. However, lack of correlation within conditions suggests that ophthalmological research has little direct effect on laypeople''s interests, which may instead be closer related to the prevalence of the respective conditions. Similar ophthalmological event analyses have previously been conducted, evaluating the effect of public health campaigns [3] , conjunctivitis epidemics [4] and Bono developing glaucoma [5] . Exploring the impact of public health campaigns for glaucoma and macular degeneration utilising Google Trends data in a New Zealand setting
cord-305195-e41yfo89	2016	The discovery of viruses as "filterable agents" in the late-nineteenth and early twentieth centuries greatly enhanced the study of viral epidemiology, allowing the characterization of infected individuals, risk factors for infection and disease, and transmission pathways. Traditional epidemiological methods measure the distribution of viral infections, diseases, and associated risk factors in populations in terms of person, place, and time using standard measures of disease frequency, study designs, and approaches to causal inference. Much can be learned about the epidemiology of viral infections using such traditional methods and many examples could be cited to establish the importance of these approaches, including demonstration of the mode of transmission of viruses by mosquitoes (e.g., yellow fever and West Nile viruses), the causal relationship between maternal viral infection and fetal abnormalities (e.g., rubella virus and cytomegalovirus), and the role of viruses in the etiology of cancer (e.g., Epstein-Barr and human papilloma viruses). The concepts and methods of infectious disease epidemiology provide the tools to understand changes in temporal and spatial patterns of viral infections and the impact of interventions.
cord-310769-y6orh217	2020	title: The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study Objective: The goal of this study is to examine, among college students in the United States, the relationship between deteriorating mental health conditions and changes in user behaviors when engaging with Google Search and YouTube during COVID-19. Conclusions: The results suggested strong discrepancies between college student groups with and without deteriorating mental health conditions in terms of behavioral changes in Google Search and YouTube usages during the COVID-19. In this study, we collected longitudinal individual-level Google Search and YouTube data from college students, and we measured their anxiety (GAD-7) and depression (PHQ-9) levels before and after the outbreak of COVID-19. First, while most of the online behavioral features we developed showed significant differences between groups of students with and without deteriorating anxiety and depressive disorders during COVID-19, our study cohort only represented a small portion of the whole population suffering from mental health difficulties.
cord-330936-qf4q8yqq	2020	A wide range of search terms were determined to represent nearly all rheumatic diseases that patients might search on Google (i.e., Osteoarthritis, gout, pseudogout, calcium pyrophosphate crystal deposition (CPPD), fibromyalgia, axial spondyloarthritis, ankylosing spondylitis, peripheral spondyloarthritis, psoriatic arthritis, reactive arthritis, septic arthritis, rheumatoid arthritis, Sjögren''s syndrome, systemic lupus erythematosus, antiphospholipid syndrome, scleroderma, polymyositis, dermatomyositis, relapsing polychondritis, familial Mediterranean fever, Tumor Necrosis Factor (TNF) Receptor-Associated Periodic Syndrome (TRAPS), Hyperimmunoglobulinemia D with Periodic Fever Syndrome (HIDS), Cryopyrin-Associated Periodic Syndromes (CAPS), vasculitis, Takayasu arteritis, giant cell arteritis, temporal arteritis, polyarteritis nodosa, Kawasaki disease, polymyalgia rheumatica, Anti-Neutrophil Cytoplasmic Antibody (ANCA)associated vasculitis, granulomatosis with polyangiitis, and Behçet''s syndrome). In the July 5-August 29, 2020 period, relative search volume of 7 of the 32 search terms (i.e., gout, fibromyalgia, peripheral spondyloarthritis, systemic lupus erythematosus, polymyositis, relapsing polychondritis, and Takayasu arteritis) statistically significantly decreased; however, 10 search terms (i.e., axial spondyloarthritis, ankylosing spondylitis, psoriatic arthritis, rheumatoid arthritis, Sjögren''s syndrome, antiphospholipid syndrome, scleroderma, Kawasaki disease, ANCA-associated vasculitis, and rheumatologist) statistically significantly increased compared to prior 4 years (Table 1 ).
cord-334751-7mdafd2y	2020	We have found that Google applications, such as Google Slides (Google LLC, 2006) and Google Forms (Google LLC, 2008) , can be particularly useful for creating digital versions of these types of instructional programs with which learners can interact, either independently or with the support of a caregiver, given their universal availability and functionality across multiple devices and operating systems. Using the Google Slides application, BCBAs can make interactive programs that learners can complete independently by incorporating praise, prompting, and error correction into the digital instructional content that are delivered automatically as the learner interacts with the activity. We will discuss and provide task analyses for the following: (a) utilizing basic functions within Google Slides (e.g., adding shapes, inserting images, linking stimuli, and protecting slides) to create interactive instructional materials, (b) developing independent instructional activities that learners can complete with minimal caregiver support, (c) developing caregiversupported instructional activities where the caregiver provides instruction using digital learning materials, and (d) organizing materials and sharing activities with clients and caregivers using Google Classroom.
cord-339309-r70zd30q	2020	20 We then discuss the CMA''s analysis of Google''s role in the open display market, where intermediaries provide various technologies that allow online publishers to sell advertising inventory and advertisers to buy it. They are able to exploit this market power by monetising the consumer attention and data through digital advertising for which they can charge high prices. By the end of the initial period of evidence gathering the CMA identified the potential foreclosure of competing BI tools with the use of Google''s web analytics and online advertising products as the main area for concern. Another important source of evidence-particularly when assessing Google''s market power-was the CMA''s Online Platforms and Digital Advertising market study that was discussed above. On the basis of this evidence the CMA concluded that Google would have the ability to use a range of non-price foreclosure mechanisms to hamper competing BI tools from accessing data from Google''s advertising and web analytics products, and from Google BigQuery.
cord-339642-3trpona9	2020	To apply this model, the authors correlated Google Trends of popular search terms with monthly reported Rubella and Measles cases from Centers for Disease Control and Prevention (CDC). Recognizing the need for up-to-date data to inform researchers, policymakers, public stakeholders, and health care providers if search queries can be used to reliably predict skin disease breakouts, we correlated Google Trends popular search terms with monthly reported Rubella and Measles cases from 2004 to 2018. So, this study provides analysis and evaluation for the association between monthly reported Rubella and Measles cases and Google Trends popular search terms that can be used to predict a future outbreak of infectious skin disease case. None of the previous studies correlated Google Trends popular search terms with certain infectious skin diseases including Rubella and Measles reported from CDC. Correlations (Pearson and Spearman) were used between Google Trends of popular search terms and monthly reported Rubella and Measles cases from CDC.
cord-348269-6z0kiapa	2020	We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). In examining associations between built environment characteristics and COVID cases, we controlled for demographic compositional characteristics of areas and population density, which has previously been utilized in econometric studies as a proxy for air pollution and other factors found with greater prevalence in urban areas [15, 16] . Additionally, previous studies found that physical disorder in the neighborhood environments is significantly associated with higher prevalence of chronic diseases [19] and poor self-rated health [20] , which also increases the chances of contracting COVID-19 [21, 22] . From GSV images, we created indicators of urban development (non-single family home, single lane roads), walkability (crosswalks, sidewalks), and physical disorder (dilapidated building, visible utility wires).
cord-351108-wfik975q	2020	We used Google Trends to analyze regional searches relating to loss of smell and taste across Italy, Spain, France, Brazil, and the United States of America and determined the association with reported Covid-19 cases. We used Google Trends to analyze regional searches relating to loss of smell and taste across Italy, Spain, France, Brazil, and the United States of America (USA) and determined the association with reported Covid-19 cases using a self-developed software programme (Python). Summary of Spearman''s rank correlation test outcomes for search interest in terms relating to anosmia and ageusia and new daily Covid-19 cases per million (both data as 7-day moving-mean) the table shows counts of regions within each country and result group. We have demonstrated that there is clear association between Google Trends search terms relating to loss of smell and taste and Covid-19 cases both on a regional, national, and international basis.
cord-351448-jowb5kfc	2020	title: The quality of online media reporting of celebrity suicide in India and its association with subsequent online suicide-related search behaviour among general population: An infodemiology study The present study aimed to assess the quality of online media reporting of a recent celebrity suicide in India and its impact on the online suicide related search behaviour of the population. Thus, in the present study we monitored the changes in internet search volumes for keywords representing suicide-seeking and help-seeking behaviours using the Google Trends platform as a proxy marker to assess the impact of recent celebrity suicide in India. Thus, the present study aimed to assess the quality of online media reporting of a celebrity suicide in India, and evaluate its adherence with the WHO guidelines for responsible media reporting of suicide. Further, the use of a novel Google Trends analysis to show an increased online search interest for suicide-seeking keywords immediately after the reference celebrity suicide provided support for the existence of Werther effect in the Indian context.