# pos

id: cord-262310-z0m6uuzf
pos: NNP
token:
  https://www.ecdc.europa.eu/en/publicationsdata/download-todays-data-geographic-distribution-covid-19-cases-worldwide

lemma:
  https://www.ecdc.europa.eu/en/publicationsdata/download-todays-data-geographic-distribution-covid-19-cases-worldwide


# ent

id: cord-018688-gvk9uazp
type: WORK_OF_ART
entity:
  The Role of Informal Digital Surveillance Systems Before During and After Infectious Disease Outbreaks A Critical Analysis


id: cord-018688-gvk9uazp
type: WORK_OF_ART
entity:
  The Semantic Processing and Integration of Distributed Electronic Resources for Epidemiology EpiSPIDER


id: cord-120442-qfgoue67
type: WORK_OF_ART
entity:
  The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students


id: cord-120442-qfgoue67
type: ORG
entity:
  Social Networking Sites Depression and Anxiety A Systematic Review Association Between Social Media Use and Depression


id: cord-120442-qfgoue67
type: WORK_OF_ART
entity:
  The Lancet Psychiatry Using Internet Based Psychological Measurement to Capture the Deteriorating Community Mental Health Profile


id: cord-120442-qfgoue67
type: WORK_OF_ART
entity:
  The Role of YouTube and the Entertainment Industry in Saving Lives by Educating and Mobilizing the Public to Adopt Behaviors for Community Mitigation of COVID 19


id: cord-120442-qfgoue67
type: WORK_OF_ART
entity:
  YouTube to Mitigate Transmission of COVID 19 Cross Sectional Study JMIR Public Health and Surveillance Misinformation


id: cord-339642-3trpona9
type: DISEASE
entity:
  influenza like symptoms 5 Research studies sought to study the association between disease outbreak and online search keywords and terms For example a study by Polgreenet al 6 examined the relationship between searches for influenza and actual influenza occurrence


id: cord-330936-qf4q8yqq
type: DISEASE
entity:
  vasculitis Takayasu arteritis giant cell arteritis temporal arteritis polyarteritis nodosa Kawasaki disease


# url

id: cord-262310-z0m6uuzf
domain: www.ecdc.europa.eu
url:
  http://www.ecdc.europa.eu/en/publicationsdata/download-todays-data-geographic-distribution-covid-19-cases-worldwide


# bib

id: cord-018632-azrqz6hf
title: Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics
author: Ganasegeran, Kurubaran
date: 2019-11-21
txt: ./txt/cord-018632-azrqz6hf.txt
cache: ./cache/cord-018632-azrqz6hf.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123557/
doi: 10.1007/978-3-030-35139-7_7
summary:
  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] .

abstract:
  The threat of emerging and re-emerging infectious diseases to global population health remains significantly enormous, and the pandemic preparedness capabilities necessary to confront such threats must be of greater potency. 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. From a systems-thinking perspective, and in today’s world of seamless boundaries and global interconnectivity, AI offers enormous potential for public health practitioners and policy makers to revolutionize healthcare and population health through focussed, context-specific interventions that promote cost-savings on therapeutic care, expand access to health information and services, and enhance individual responsibility for their health and well-being. This chapter systematically appraises the dawn of AI technology towards empowering population health to combat the rise of infectious disease epidemics.


id: cord-018688-gvk9uazp
author: Magid, Avi
date: 2018-03-23
txt: ./txt/cord-018688-gvk9uazp.txt
cache: ./cache/cord-018688-gvk9uazp.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123634/
doi: 10.1007/978-94-024-1263-5_14
summary:
  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.

title:
  The Role of Informal Digital Surveillance Systems Before, During and After Infectious Disease Outbreaks: A Critical Analysis

abstract:
  Background One of the main limitations of traditional surveillance systems for disease detection is their inability to detect epidemics in real-time. In addition to syndromic surveillance, a number of informal digital resources have been developed. These systems are based on data collected through media sources such as news reports on the Internet, mailing lists, and RSS (Really Simple Syndication) feeds. The role of such systems at all stages of the epidemic remains unclear. Methods A literature review was carried out on informal digital resources for infectious disease surveillance. 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. Results Informal digital resources use similar sources of data for surveillance. However, they use different algorithms to create their output, and cover different geographic areas. In this regard, they complement each other with respect to information completeness. There is evidence in the literature on the systems’ usefulness in communicating information to public health professionals, as well as to the general public during and after previous epidemics. Retrospective studies of some systems have shown a theoretical decrease in the time of epidemic detection compared to conventional surveillance. However, there is no evidence of the ability for real-time detection. Conclusions Currently, there is little prospective evidence that existing informal systems are capable of real-time early detection of disease outbreaks. Most systems accumulate large amounts of information on a wide variety of diseases, making it difficult to extract critical information. Presenting critical information clearly and precisely remains a challenge.


id: cord-021088-9u3kn9ge
title: Awaiting the Second Big Data Revolution: From Digital Noise to Value Creation
author: Huberty, Mark
date: 2015-02-18
txt: ./txt/cord-021088-9u3kn9ge.txt
cache: ./cache/cord-021088-9u3kn9ge.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7149011/
doi: 10.1007/s10842-014-0190-4
summary:
  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.

abstract:
  “Big data”—the collection of vast quantities of data about individual behavior via online, mobile, and other data-driven services—has been heralded as the agent of a third industrial revolution—one with raw materials measured in bits, rather than tons of steel or barrels of oil. Yet the industrial revolution transformed not just how firms made things, but the fundamental approach to value creation in industrial economies. To date, big data has not achieved this distinction. Instead, today’s successful big data business models largely use data to scale old modes of value creation, rather than invent new ones altogether. Moreover, today’s big data cannot deliver the promised revolution. In this way, today’s big data landscape resembles the early phases of the first industrial revolution, rather than the culmination of the second a century later. Realizing the second big data revolution will require fundamentally different kinds of data, different innovations, and different business models than those seen to date. That fact has profound consequences for the kinds of investments and innovations firms must seek, and the economic, political, and social consequences that those innovations portend.


id: cord-120442-qfgoue67
author: Zaman, Anis
date: 2020-09-05
txt: ./txt/cord-120442-qfgoue67.txt
cache: ./cache/cord-120442-qfgoue67.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.09076v1.pdf
doi: nan
summary:
  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.

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

abstract:
  Mental health problems among the global population are worsened during the coronavirus disease (COVID-19). How individuals engage with online platforms such as Google Search and YouTube undergoes drastic shifts due to pandemic and subsequent lockdowns. Such ubiquitous daily behaviors on online platforms have the potential to capture and correlate with clinically alarming deteriorations in mental health profiles in a non-invasive manner. 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. This study recruited a cohort of 49 students from a U.S. college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant. This study followed up with the same cohort during May 2020 (during the pandemic), and the anxiety and depression levels were assessed again. The longitudinal Google Search and YouTube history data were anonymized and collected. From individual-level Google Search and YouTube histories, we developed 5 signals that can quantify shifts in online behaviors during the pandemic. We then assessed the differences between groups with and without deteriorating mental health profiles in terms of these features. Significant features included late-night online activities, continuous usages, and time away from the internet, porn consumptions, and keywords associated with negative emotions, social activities, and personal affairs. Though further studies are required, our results demonstrated the feasibility of utilizing pervasive online data to establish non-invasive surveillance systems for mental health conditions that bypasses many disadvantages of existing screening methods.


id: cord-193136-7g6qr73e
title: Visible Insights of the Invisible Pandemic: A Scientometric, Altmetric and Topic Trend Analysis
author: Bhattacharya, Sujit
date: 2020-04-22
txt: ./txt/cord-193136-7g6qr73e.txt
cache: ./cache/cord-193136-7g6qr73e.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.10878v1.pdf
doi: nan
summary:
  (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.

abstract:
  The recent SARS-COV-2 virus outbreak has created an unprecedented global health crisis! The disease is showing alarming trends with the number of people getting infected with this disease, new cases and death rate are all highlighting the need to control this disease at the earliest. The strategy now for the governments around the globe is how to limit the spread of the virus until the research community develops treatment/drug or vaccination against the virus. The outbreak of this disease has unsurprisingly led to huge volume of research within a short period of time surrounding this disease. It has also led to aggressive social media activity on twitter, Facebook, dedicated blogs, news reports and other online sites actively involved in discussing about the various aspects of and related to this disease. It becomes a useful and challenging exercise to draw from this huge volume of research, the key papers that form the research front, its influence in the research community, and other important research insights. Similarly, it becomes important to discern the key issues that influence the society concerning this disease. The paper is motivated by this. It attempts to distinguish which are the most influential papers, the key knowledge base and major topics surrounding the research covered by COVID-19. Further it attempts to capture the society's perception by discerning key topics that are trending online. The study concludes by highlighting the implications of this study.


id: cord-232959-jcnvnn2k
title: Private Sources of Mobility Data Under COVID-19
author: Arnal, Raquel P''erez
date: 2020-07-14
txt: ./txt/cord-232959-jcnvnn2k.txt
cache: ./cache/cord-232959-jcnvnn2k.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.07095v1.pdf
doi: nan
summary:
  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).

abstract:
  The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility is at the epicenter of that change, as the greatest facilitator for the spread of the virus. To study the change in mobility, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to possible future crisis, we need to properly understand all mobility data sources at our disposal. Our work is dedicated to the study of private mobility sources, gathered and released by large technological companies. This data is of special interest because, unlike most public sources, it is focused on people, not transportation means. i.e., its unit of measurement is the closest thing to a person in a western society: a phone. Furthermore, the sample of society they cover is large and representative. On the other hand, this sort of data is not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we set forth to explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting because of its large and fast pandemic peak, and for its implementation of a sustained, generalized lockdown. We find private mobility sources to be both correlated and complementary. Using them, we evaluate the efficiency of implemented policies, and provide a insights into what new normal means in Spain.


id: cord-252218-jrgl0x06
author: Heerfordt, C.
date: 2020-04-20
txt: ./txt/cord-252218-jrgl0x06.txt
cache: ./cache/cord-252218-jrgl0x06.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0033350620301177
doi: 10.1016/j.puhe.2020.04.012
abstract: [Figure: see text]
summary:
  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

title:
  Has there been an increased interest in smoking cessation during the first months of the COVID-19 pandemic? A Google Trends study


id: cord-256094-f85xc5uu
author: Milinovich, Gabriel J
date: 2014-12-31
txt: ./txt/cord-256094-f85xc5uu.txt
cache: ./cache/cord-256094-f85xc5uu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/25551277/
doi: 10.1186/s12879-014-0690-1
summary:
  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).

title:
  Using internet search queries for infectious disease surveillance: screening diseases for suitability

abstract:
  BACKGROUND: Internet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. 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. METHODS: Official notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009–13 using Spearman’s rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems. RESULTS: Notifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems. CONCLUSIONS: The findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-014-0690-1) contains supplementary material, which is available to authorized users.


id: cord-262310-z0m6uuzf
title: Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis
author: Effenberger, Maria
date: 2020-04-17
txt: ./txt/cord-262310-z0m6uuzf.txt
cache: ./cache/cord-262310-z0m6uuzf.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32305520/
doi: 10.1016/j.ijid.2020.04.033
summary:
  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 ).

abstract:
  Abstract Objectives To assess the association of public interest in coronavirus infections with the actual number of infected cases for selected countries across the globe. 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. Results Worldwide public interest in Coronavirus reached its first peak end of January when numbers of newly infected patients started to increase exponentially in China. 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. At this time the general interest in China but also the Republic of Korea has already been significantly decreased as compared to end of January. Correlations between RSV indices and number of new COVID-19 cases were observed across all investigated countries with highest correlations observed with a time lag of -11.5 days, i.e. highest interest in coronavirus observed 11.5 days before the peak of newly infected cases. This pattern was very consistent across European countries but also holds true for the US. In Brazil and Australia, highest correlations were observed with a time lag of -7 days. In Egypt the highest correlation is given with a time lag of 0, potentially indicating that in this country, numbers of newly infected patients will increase exponentially within the course of April. Conclusions Public interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic. Public interest is on average highest 11.5 days before the peak of newly infected cases.


id: cord-265178-q7x7ec24
title: Fear of the coronavirus and the stock markets
author: Lyócsa, Štefan
date: 2020-08-26
txt: ./txt/cord-265178-q7x7ec24.txt
cache: ./cache/cord-265178-q7x7ec24.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32868975/
doi: 10.1016/j.frl.2020.101735
summary:
  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.

abstract:
  Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines amid high uncertainty. In this paper, we use Google search volume activity as a gauge of panic and fear. The chosen search terms are specific to the coronavirus crisis and correspond to phrases related to nonpharmaceutical intervention policies to fight physical contagion. 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.


id: cord-289647-14ba5sro
author: Panuganti, Bharat A.
date: 2020-06-02
txt: ./txt/cord-289647-14ba5sro.txt
cache: ./cache/cord-289647-14ba5sro.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1177/0194599820932128
doi: 10.1177/0194599820932128
summary:
  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.

title:
  Predicting COVID-19 Incidence Using Anosmia and Other COVID-19 Symptomatology: Preliminary Analysis Using Google and Twitter

abstract:
  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. To describe the effect of mass media communications on Twitter and Google Search user trends. STUDY DESIGN: Retrospective observational study. SETTING: United States. SUBJECTS AND METHODS: Google Search and “tweet” frequency concerning COVID-19, smell, and nonsmell symptoms of COVID-19 generated between January 1 and April 8, 2020, were collected using Google Trends and Crimson Hexagon, respectively. Spearman coefficients linking each of these user trends to COVID-19 incidence were compared. Correlations obtained after excluding a short timeframe (March 22 to March 24) corresponding to the publication of a widely read lay media publication reporting anosmia as a symptom of infection was performed for comparative analysis. RESULTS: Google searches and tweets concerning all nonsmell symptoms (0.744 and 0.761, respectively) and COVID-19 (0.899 and 0.848) are more strongly correlated with disease incidence than smell loss (0.564 and 0.539). Twitter users tweeting about smell loss during the study period were more likely to be female (52%) than users tweeting about COVID-19 more generally (47%). Tweet and Google Search frequency pertaining to smell loss increased significantly (>2.5 standard deviations) following a widely read media publication linking smell loss and SARS-CoV-2 infection. CONCLUSIONS: Google Search and tweet frequency regarding fever and shortness of breath are more robust indicators of COVID-19 incidence than anosmia. Mass media communications represent important confounders that should be considered in future analyses.


id: cord-294955-bybdn9yb
title: Peaks in online inquiries into pharyngitis-related symptoms correspond with annual incidence rates
author: Brkic, Faris F.
date: 2020-09-23
txt: ./txt/cord-294955-bybdn9yb.txt
cache: ./cache/cord-294955-bybdn9yb.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32968893/
doi: 10.1007/s00405-020-06362-4
summary:
  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.

abstract:
  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. METHODS: Google Trends (GT) was utilized for systematic acquisition of pharyngitis-related search terms (sore throat, cough, fever, cold). Six countries from both hemispheres including four English (United Kingdom, United States, Canada, and Australia) and two non-English speaking countries (Austria and Germany) were selected for further analysis. Time series data on relative search interest for pharyngitis-related search terms, covering a timeframe between 2004 and 2019 were extracted. Following reliability analysis using the intra-class correlation coefficient, the cosinor time series analysis was utilized to determine annual peaks in public-inquiries. RESULTS: The extracted datasets of GT proved to be highly reliable with correlation coefficients ranging from 0.83 to 1.0. Graphical visualization showed annual seasonal peaks for pharyngitis-related search terms in all included countries. The cosinor time series analysis revealed these peaks to be statistically significant during winter months (all p < 0.001). CONCLUSION: Our study revealed seasonal variations for pharyngitis-related terms which corresponded to winter incidence peaks of acute pharyngitis. These results highlight the need for easily accessible information on diagnosis, therapy, and red-flag symptoms for this common disease. Accurately informed patients might contribute to a reduction of unnecessary clinic visits and potentially cutback the futile antibiotic overuse. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00405-020-06362-4) contains supplementary material, which is available to authorized users.


id: cord-296821-qdhj9zj6
title: Interests in quitting smoking and alcohol during COVID‐19 pandemic in India: A Google Trends study
author: Uvais, Nalakath A.
date: 2020-07-19
txt: ./txt/cord-296821-qdhj9zj6.txt
cache: ./cache/cord-296821-qdhj9zj6.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1111/pcn.13118
doi: 10.1111/pcn.13118
abstract: nan
summary:
  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.


id: cord-297835-ukrz8tlv
author: Leith, Douglas J.
date: 2020-09-30
txt: ./txt/cord-297835-ukrz8tlv.txt
cache: ./cache/cord-297835-ukrz8tlv.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32997724/
doi: 10.1371/journal.pone.0239943
summary:
  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.

title:
  Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a light-rail tram

abstract:
  We report on the results of a Covid-19 contact tracing app measurement study carried out on a standard design of European commuter tram. Our measurements indicate that in the tram there is little correlation between Bluetooth received signal strength and distance between handsets. We applied the detection rules used by the Italian, Swiss and German apps to our measurement data and also characterised the impact on performance of changes in the parameters used in these detection rules. We find that the Swiss and German detection rules trigger no exposure notifications on our data, while the Italian detection rule generates a true positive rate of 50% and a false positive rate of 50%. Our analysis indicates that the performance of such detection rules is similar to that of triggering notifications by randomly selecting from the participants in our experiments, regardless of proximity.


id: cord-298953-9aifql2f
title: The Value of Greenspace Under Pandemic Lockdown
author: Day, Brett H.
date: 2020-08-04
txt: ./txt/cord-298953-9aifql2f.txt
cache: ./cache/cord-298953-9aifql2f.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32836861/
doi: 10.1007/s10640-020-00489-y
summary:
  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.

abstract:
  The COVID-19 outbreak resulted in unprecedented restrictions on citizen’s freedom of movement as governments moved to institute lockdowns designed to reduce the spread of the virus. While most out-of-home leisure activities were prohibited, in England the lockdown rules allowed for restricted use of outdoor greenspace for the purposes of exercise and recreation. In this paper, we use data recorded by Google from location-enabled mobile devices coupled with a detailed recreation demand model to explore the welfare impacts of those constraints on leisure activities. Our analyses reveals evidence of large-scale substitution of leisure time towards recreation in available greenspaces. Indeed, despite the restrictions the economic value of greenspace to the citizens of England fell by only £150 million over lockdown. Examining the outcomes of counterfactual policies we find that the imposition of stricter lockdown rules would have reduced welfare from greenspace by £1.14 billion. In contrast, more relaxed lockdown rules would have delivered an aggregate increase in the economic value of greenspace equal to £1.47 billion.


id: cord-302758-i5pe61h1
title: Otolaryngology-related Google Search trends during the COVID-19 pandemic
author: Pier, Matthew M.
date: 2020-06-19
txt: ./txt/cord-302758-i5pe61h1.txt
cache: ./cache/cord-302758-i5pe61h1.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32659612/
doi: 10.1016/j.amjoto.2020.102615
summary:
  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.

abstract:
  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. MATERIALS AND METHODS: In this cross-sectional study, data on Google Search queries in the United States for 30 otolaryngology-related terms were obtained from Google Trends. The means of relative search volume from the COVID-19 period (March 29, 2020 through May 16, 2020) were compared to similar periods from 2016 to 2019 using a t-test of two independent samples. RESULTS: In total, 16.6% of search terms had significant increases in relative search volume during the COVID-19 period, with the largest percentage increase for “can't smell” (124.4%, p = .006), followed by “allergies” (30.3%, p = .03), “voice pain” (26.1%, p = .008), and “ears ringing” (19.0%, p < .001). Of all search terms, 26.7% had significant decreases in relative search volume, including the largest percentage decrease for “laryngitis” (59.8%, p < .001), followed by “thyroid nodule” (54.4%, p < .001), “thyroid cancer” (45.6%, p < .001), and “ENT” (34.9%, p < .001). 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. With reduced access to in-office otolaryngology care in the United States during the COVID-19 pandemic, these are important considerations for otolaryngology practices to meet the needs of patients who lack access to care.


id: cord-304183-zv3s7cjq
title: Evaluating the mainstream impact of ophthalmological research with Google Trends
author: Thirunavukarasu, Arun James
date: 2020-11-01
txt: ./txt/cord-304183-zv3s7cjq.txt
cache: ./cache/cord-304183-zv3s7cjq.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1038/s41433-020-01257-4
doi: 10.1038/s41433-020-01257-4
abstract: nan
summary:
  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


id: cord-305195-e41yfo89
title: Viral Epidemiology: Tracking Viruses with Smartphones and Social Media
author: Rainwater-Lovett, Kaitlin
date: 2016-02-12
txt: ./txt/cord-305195-e41yfo89.txt
cache: ./cache/cord-305195-e41yfo89.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/B9780128009642000185
doi: 10.1016/b978-0-12-800964-2.00018-5
summary:
  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.

abstract:
  The science of epidemiology has been developed over the last 200 years, using traditional methods to describe the distribution of diseases by person, place, and time. However, in the last several decades, a new set of technologies has become available, based on the methods of computer sciences, systems biology, and the extraordinary powers of the Internet. Technological and analytical advances can enhance traditional epidemiological methods to study the emergence, epidemiology, and transmission dynamics of viruses and associated diseases. Social media are increasingly used to detect the emergence and geographic spread of viral disease outbreaks. Large-scale population movement can be estimated using satellite imagery and mobile phone use, and fine-scale population movement can be tracked using global positioning system loggers, allowing estimation of transmission pathways and contact patterns at different spatial scales. Advances in genomic sequencing and bioinformatics permit more accurate determination of viral evolution and the construction of transmission networks, also at different spatial and temporal scales. Phylodynamics links evolutionary and epidemiological processes to better understand viral transmission patterns. More complex and realistic mathematical models of virus transmission within human and animal populations, including detailed agent-based models, are increasingly used to predict transmission patterns and the impact of control interventions such as vaccination and quarantine. In this chapter, we will briefly review traditional epidemiological methods and then describe the new technologies with some examples of their application.


id: cord-310769-y6orh217
author: Zaman, A.
date: 2020-08-25
txt: ./txt/cord-310769-y6orh217.txt
cache: ./cache/cord-310769-y6orh217.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.08.22.20178640
doi: 10.1101/2020.08.22.20178640
summary:
  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.

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

abstract:
  Background: Mental health problems among the global population are worsened during the coronavirus disease (COVID-19). Yet, current methods for screening mental health issues rely on in-person interviews, which can be expensive, time-consuming, blocked by social stigmas and quarantines. Meanwhile, how individuals engage with online platforms such as Google Search and YouTube undergoes drastic shifts due to COVID-19 and subsequent lockdowns. Such ubiquitous daily behaviors on online platforms have the potential to capture and correlate with clinically alarming deteriorations in mental health profiles of users through a non-invasive manner. 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. Methods: This study recruited a cohort of undergraduate students (N=49) from a U.S. college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant. The anxiety level was assessed via the General Anxiety Disorder-7 (GAD-7). The depression level was assessed via the Patient Health Questionnaire-9 (PHQ-9). This study followed up with the same cohort during May 2020 (during the pandemic), and the anxiety and depression levels were assessed again. The longitudinal Google Search and YouTube history data of all participants were anonymized and collected. From individual-level Google Search and YouTube histories, we developed 5 signals that can quantify shifts in online behaviors during the pandemic. We then assessed the differences between groups with and without deteriorating mental health profiles in terms of these features. Results: Of the 49 participants, 41% (n=20) of them reported a significant increase (increase in the PHQ-9 score > 5) in depression, denoted as DEP; 45% (n=22) of them reported a significant increase (increase in the GAD-7 score > 5) in anxiety, denoted as ANX. Of the 5 features proposed to quantify online behavior changes, statistical significances were found between the DEP and non-DEP groups for all of them (P<.01, effect sizes eta_{partial}^2 ranging between 0.130 to 0.320); statistical significances were found between the ANX and non-ANX groups for 4 of them (P<.02, effect sizes eta_{partial}^2 ranging between 0.115 to 0.231). Significant features included late-night online activities, continuous usages and time away from the internet, porn consumptions, and keywords associated with negative emotions, social activities, and personal affairs. 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. Though further studies are required, our results demonstrated the feasibility of utilizing pervasive online data to establish non-invasive surveillance systems for mental health conditions that bypasses many disadvantages of existing screening methods.


id: cord-330936-qf4q8yqq
author: Kardeş, Sinan
date: 2020-10-18
txt: ./txt/cord-330936-qf4q8yqq.txt
cache: ./cache/cord-330936-qf4q8yqq.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33070255/
doi: 10.1007/s00296-020-04728-9
summary:
  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 ).

title:
  Public interest in rheumatic diseases and rheumatologist in the United States during the COVID-19 pandemic: evidence from Google Trends

abstract:
  To evaluate the public interest in rheumatic diseases during the coronavirus disease 2019 (COVID-19) pandemic. Google Trends was queried to analyze search trends in the United States for numerous rheumatic diseases and also the interest in a rheumatologist. Three 8-week periods in 2020 ((March 15–May 9), (May 10–July 4), and (July 5–August 29)) were compared to similar periods of the prior 4 years (2016–2019). Compared to a similar time period between 2016 and 2019, a significant decrease was found in the relative search volume for more than half of the search terms during the initial March 15–May 9, 2020 period. However, this trend appeared to reverse during the July 5–August 29, 2020 period where the relative volume for nearly half of the search terms were not statistically significant compared to similar periods of the prior 4 years. In addition, this period showed a significant increase in relative volume for the terms: Axial spondyloarthritis, ankylosing spondylitis, psoriatic arthritis, rheumatoid arthritis, Sjögren’s syndrome, antiphospholipid syndrome, scleroderma, Kawasaki disease, Anti-Neutrophil Cytoplasmic Antibody (ANCA)-associated vasculitis, and rheumatologist. There was a significant decrease in relative search volume for many rheumatic diseases between March 15 and May 9, 2020 when compared to similar periods during the prior 4 years. However, the trends reversed after the initial period ended. There was an increase in relative search for the term “rheumatologist” between July and August 2020 suggesting the need for rheumatologists during the COVID-19 pandemic. Policymakers and healthcare providers should address the informational demands on rheumatic diseases and needs for rheumatologists by the general public during pandemics like COVID-19.


id: cord-334751-7mdafd2y
title: Creating and Sharing Digital ABA Instructional Activities: A Practical Tutorial
author: Mattson, Stephanie L.
date: 2020-07-23
txt: ./txt/cord-334751-7mdafd2y.txt
cache: ./cache/cord-334751-7mdafd2y.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32837705/
doi: 10.1007/s40617-020-00440-z
summary:
  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.

abstract:
  Board Certified Behavior Analysts (BCBAs) may encounter situations, such as the current COVID-19 pandemic, that preclude them from providing traditional in-person applied behavior-analytic services to clients. When conditions prevent BCBAs and behavior technicians from working directly with clients, digital instructional activities designed by BCBAs and delivered via a computer or tablet may be a viable substitute. Google applications, including Google Slides, Google Forms, and Google Classroom, can be particularly useful for creating and sharing digital instructional activities. In the current article, we provide task analyses for utilizing basic Google Slides functions, developing independent instructional activities, developing caregiver-supported instructional activities, and sharing activities with clients and caregivers. We also provide practical recommendations for implementing digital instructional activities with clients and caregivers.


id: cord-339309-r70zd30q
title: Recent Developments at the CMA: 2019–2020
author: Havell, Richard
date: 2020-10-06
txt: ./txt/cord-339309-r70zd30q.txt
cache: ./cache/cord-339309-r70zd30q.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33041502/
doi: 10.1007/s11151-020-09790-y
summary:
  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.

abstract:
  We discuss three important cases that the Competition and Markets Authority (CMA) has completed over the past year: First, the coronavirus pandemic has had implications for a wide range of the CMA’s work; we describe the work on price gouging conducted by the CMA’s Covid-19 taskforce and respond to the argument that competition authorities should not be concerned about such behaviour. Second, a number of high-profile studies have considered the appropriate application of competition policy in digital industries. The second two cases—the Online Platforms and Digital Advertising market study, and the Google/Looker merger—show the work the CMA has continued to do in this area.


id: cord-339642-3trpona9
author: Obeidat, Rand
date: 2020-06-08
txt: ./txt/cord-339642-3trpona9.txt
cache: ./cache/cord-339642-3trpona9.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1177/1178633720928356
doi: 10.1177/1178633720928356
summary:
  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.

title:
  Can Users Search Trends Predict People Scares or Disease Breakout? An Examination of Infectious Skin Diseases in the United States

abstract:
  BACKGROUND: In health and medicine, people heavily use the Internet to search for information about symptoms, diseases, and treatments. As such, the Internet information can simulate expert medical doctors, pharmacists, and other health care providers. AIM: This article aims to evaluate a dataset of search terms to determine whether search queries and terms can be used to reliably predict skin disease breakouts. Furthermore, the authors propose and evaluate a model to decide when to declare a particular month as Epidemic at the US national level. METHODS: A Model was designed to distinguish a breakout in skin diseases based on the number of monthly discovered cases. 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). Regressions and decision trees were used to determine the impact of different terms to trigger the occurrence of epidemic classes. RESULTS: Results showed that the volume of search keywords for Rubella and Measles rises when the volume of those reported diseases rises. Results also implied that the overall process was successful and should be repeated with other diseases. Such process can trigger different actions or activities to be taken when a certain month is declared as “Epidemic.” Furthermore, this research has shown great interest for vaccination against Measles and Rubella. CONCLUSIONS: The findings suggest that the search queries and keyword trends can be truly reliable to be used for the prediction of disease outbreaks and some other related knowledge extraction applications. Also search-term surveillance can provide an additional tool for infectious disease surveillance. Future research needs to re-apply the model used in this article, and researchers need to question whether characterizing the epidemiology of Coronavirus Disease 2019 (COVID-19) pandemic waves in United States can be done through search queries and keyword trends.


id: cord-348269-6z0kiapa
author: Nguyen, Quynh C.
date: 2020-09-01
txt: ./txt/cord-348269-6z0kiapa.txt
cache: ./cache/cord-348269-6z0kiapa.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.3390/ijerph17176359
doi: 10.3390/ijerph17176359
summary:
  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).

title:
  Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

abstract:
  The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. 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). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.


id: cord-351108-wfik975q
author: Cherry, George
date: 2020-07-16
txt: ./txt/cord-351108-wfik975q.txt
cache: ./cache/cord-351108-wfik975q.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1080/14787210.2020.1792289
doi: 10.1080/14787210.2020.1792289
summary:
  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.

title:
  Loss of smell and taste: a new marker of COVID-19? Tracking reduced sense of smell during the coronavirus pandemic using search trends

abstract:
  OBJECTIVES: It has been demonstrated that reduction in smell and/or taste is the most predictive symptom in SARS-CoV-2/Covid-19 infection. 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. METHODS: In order to retrieve the data, we built a Python software program that provides access to Google Trends data via an application program interface. Daily COVID-19 case data for subregions of the five countries selected were retrieved from respective national health authorities. We sought to assess the association between raw search interest data and COVID-19 new daily cases per million for all regions individually. RESULTS: In total, we yielded 2188 sets of Google Trends data which included 548 time series of 4 anosmia and ageusia search concepts over the study period for 137 regions. These data indicated that differences in search interest for terms relating to anosmia and ageusia, between regions, is associated with geographical trends in new Covid-19 cases. CONCLUSIONS: We feel that Google search trends relating to loss of smell can be utilized to identify potential Covid-19 outbreaks on a national and regional basis.


id: cord-351448-jowb5kfc
author: Ganesh, Ragul
date: 2020-08-29
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url: https://api.elsevier.com/content/article/pii/S1876201820304937
doi: 10.1016/j.ajp.2020.102380
summary:
  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.

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

abstract:
  The literature reports increased suicide rates among general population in the weeks following the celebrity suicide, known as the Werther effect. The World Health Organization (WHO) has developed guidelines for responsible media reporting of suicide. 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. A total of 200 online media reports about Sushant Singh Rajput’s suicide published between 14(th) to 20(th) June, 2020 were assessed for quality of reporting following the checklist prepared using the WHO guidelines. Further, we examined the change in online suicide-seeking and help-seeking search behaviour of the population following celebrity suicide for the month of June using selected keywords. In terms of potentially harmful media reportage, 85.5% of online reports violated at least one WHO media reporting guideline. In terms of potentially helpful media reportage, only 13% articles provided information about where to seek help for suicidal thoughts or ideation. There was a significant increase in online suicide-seeking (U = 0.5, p < 0.05) and help-seeking (U = 6.5, p < 0.05) behaviour after the reference event, when compared to baseline. However, the online peak search interest for suicide-seeking was greater than help-seeking. This provides support for a strong Werther effect, possibly associated with poor quality of media reporting of celebrity suicide. There is an urgent need for taking steps to improve the quality of media reporting of suicide in India.


