scholarly journals The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis

10.2196/22880 ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e22880
Author(s):  
Milad Asgari Mehrabadi ◽  
Nikil Dutt ◽  
Amir M Rahmani
Keyword(s):  
United States ◽  
Granger Causality ◽  
Public Interest ◽  
Granger Causality Test ◽  
The United States ◽  
Google Trends ◽  
Causality Test ◽  
Us States ◽  
Search Queries ◽  
Google Search

Background The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. Objective The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. Methods To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. Results Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. Conclusions Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.

scholarly journals The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis (Preprint)

2020 ◽  
Author(s):  
Milad Asgari Mehrabadi ◽  
Nikil Dutt ◽  
Amir M. Rahmani
Keyword(s):  
United States ◽  
Public Interest ◽  
Pearson Correlation ◽  
The United States ◽  
Google Trends ◽  
Causality Test ◽  
Search Queries ◽  
The Us ◽  
Google Search ◽  
The Impact

BACKGROUND The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. OBJECTIVE The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to the increase of daily cases in the US. In particular, we were concerned with searches for dine-in restaurants and bars. Data were obtained from Google trends API and COVID tracking project. METHODS To test causation of one time series on another, we used Granger’s Causality Test. We considered the causation of two different search query trends, namely restaurant and bars, on daily positive cases in top-10 states/territories of the United States with the highest and lowest daily new positive cases. In addition, to measure the linear relation of different trends, we used Pearson correlation. RESULTS Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average. California, for example, had most searches for restaurants on June 7th, 2020, which affected the number of new cases within two weeks after the peak with the P-value of .004 for Granger’s causality test. CONCLUSIONS Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases for regions with higher numbers of daily new cases in the United States. We showed that such influential search trends could be used as additional information for prediction tasks in new cases of each region. This prediction can help healthcare leaders manage and control the impact of COVID-19 outbreaks on society and be prepared for the outcomes.

Causal Relationship between Asset Prices and Output in the United States: Evidence from the State-Level Panel Granger Causality Test

2015 ◽  
Vol 50 (10) ◽  
pp. 1728-1741 ◽  
Author(s):  
Furkan Emirmahmutoglu ◽  
Mehmet Balcilar ◽  
Nicholas Apergis ◽  
Beatrice D. Simo-Kengne ◽  
Tsangyao Chang ◽  
...  
Keyword(s):  
United States ◽  
Causal Relationship ◽  
Granger Causality ◽  
Asset Prices ◽  
State Level ◽  
Granger Causality Test ◽  
The United States ◽  
The State ◽  
Causality Test ◽  
Panel Granger Causality

scholarly journals Increase in Google Trends Regarding Telogen Effluvium Due To COVID-19

2021 ◽  
Vol 5 (2) ◽  
pp. 137-139
Author(s):  
Jasmine Garg ◽  
Abigail Cline ◽  
Frederick Pereira
Keyword(s):  
United States ◽  
Hair Loss ◽  
Public Interest ◽  
The United States ◽  
Google Trends ◽  
Therapeutic Interventions ◽  
The Public ◽  
Search Terms ◽  
The Future ◽  
Before And After

Objective: The purpose of this study was to assess the public interest in the United States of telogen effluvium before and after the COVID-19 pandemic in order to investigate the best therapeutic interventions for dermatologists in the future. Methods: We performed Google TrendsTM search for “COVID hair loss”, “telogen effluvium” and “hair loss” between 5/1/20 and 8/16/20. Conclusion: All three terms have increased in popularity for search terms since mid-March and were the most prevalent in the states that experienced the earliest increase in number of coronavirus cases.

Online Search Behaviour of Seeking Support for Obsessive Compulsive Disorder and Its Impact on Unemployment Level of the United States: Evidence from Google Search Queries (Preprint)

2019 ◽  
Author(s):  
Chamil W Senarathne ◽  
Wei Jianguo
Keyword(s):  
United States ◽  
The United States ◽  
Aggregate Data ◽  
Online Search ◽  
Search Behaviour ◽  
Search Queries ◽  
Compulsive Disorder ◽  
Google Search ◽  
The Impact ◽  
Age Category

BACKGROUND People have access to a massive volume of up-to-date health information processed by various search engines. Before seeing a doctor, people are used to seek information about identification and support available (e.g. doctors, support centers. forum discussions etc.) for their disorder/s online. Researchers have shown that Internet search queries contain much valuable information about the disequilibrium dynamics of various economics activities (e.g. employment, consumption). OCD as a disorder steals much of the valuable time, energy and effort in day-to-day work life and scholars argues that patients diagnosed with OCD may have higher unemployment rates and lower average income. Except for a handful of work examining the relationship between various disorders (e.g. cancer) and online search volume data, the direct linkage between online search behaviour of seeking support for OCD and unemployment in the United States has been completely ignored in the literature. OBJECTIVE The objective of this paper is to examine the impact of online search behaviour of identifying and seeking support for OCD on unemployment level of the United States at aggregate data and age category level. METHODS This paper analyzes 50 closely related online search terms on identifying and seeking support for OCD from March 2006 to June 2019. Ordinary least squares technique is used to identify the significance of the impact of search behaviour on the unemployment levels of the United States. After screening for instrumentality, a reduced version of regression is derived after treating for multicollinearity among regression variables. In order to eliminate the effect of searches made by people other than employed people who have subsequently been unemployed, a diagnostic regression is run. RESULTS The findings show that online search behaviour of identifying and seeking support for OCD significantly impacts unemployment level of the United States at overall regression level (p<0.01, R^2=73%) and age category level regressions (p<0.01, average R^2=66%). Moreover, the diagnostic test confirms that the regression on aggregate data and age category level data properly explains the underlying relationship as hypothesized because the coefficient of Google search queries driven (the effect) by employed population is positive and highly significant in explaining the unemployment level of the United States (p<0.01, average R^2=90%). CONCLUSIONS The findings of this study are helpful for policymakers and regulators in providing useful inputs for designing and administering programms on prevention and counseling OCD diagnosed working population of the United States. In particular, this paper is helpful in identifying the age categories of male and female employed population who are searching and seeking support on OCD. The government institutions in the USA must utilize online search queries for effective analysis and identification of different age category of people who are in need of support. Since search query data are available at country-level and regional level, this could easily be done by IT rather than population surveys that are costly and time consuming.

Predicting the Number of Suicides in Japan: A Vector Autoregression Time Series Model Using Internet Search Queries (Preprint)

2021 ◽  
Author(s):  
Kazuya Taira ◽  
Rikuya Hosokawa ◽  
Tomoya Itatani ◽  
Sumio Fujita
Keyword(s):  
Granger Causality ◽  
Vector Autoregression ◽  
Unit Roots ◽  
Granger Causality Test ◽  
Lagrangian Multiplier ◽  
Internet Search ◽  
Causality Test ◽  
Search Queries ◽  
Original Series ◽  
Lm Test

BACKGROUND The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is critical to take timely preventive measures. OBJECTIVE In this study, we examine whether the number and characteristics of suicides can be predicted based on the Internet search behavior and the search queries. METHODS The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender on the queries associated with "suicide" on "Yahoo Search" from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to "suicide" were searched before searching for the keyword "suicide," and extracted and used for analyses: "abuse," "work, don’t want to go," "company, want to quit," "divorce," and "no money." The Augmented Dickey–Fuller and Johansen's tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch–Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque–Bera (JB) test were employed to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. RESULTS In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: −9.24, max tau 3: −5.38) and women (minimum tau 3: −9.24, max tau 3: −5.38) had no unit roots for all variables. In Johansen's test, a cointegration relationship was observed among several variables. The queries used in the converged models were "divorce" for men (BG-LM test: p= 0.55; ARCH-LM test: p= 0.63; JB test: p= 0.66) and "no money" for women (BG-LM test: p = 0.17; ARCH-LM test: p = 0.15; JB test: p= 0.10). In the Granger causality test for each variable, "divorce" was significant for both men (F= 3.29, p = 0.041) and women (F = 3.23, p = 0.044). ¬ CONCLUSIONS The number of suicides can be predicted by the search queries related to the keyword "suicide." Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on "no money" and "divorce" predict suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary.

Public Health Implications of Google Searches for Sunscreen, Sunburn, Skin Cancer, and Melanoma in the United States

2018 ◽  
Vol 33 (4) ◽  
pp. 611-615 ◽  
Author(s):  
Zachary H. Hopkins ◽  
Aaron M. Secrest
Keyword(s):  
Public Health ◽  
United States ◽  
Skin Cancer ◽  
Search Engine ◽  
State Level ◽  
The United States ◽  
Google Trends ◽  
Search Term ◽  
Melanoma Incidence ◽  
Google Search

Purpose: Google Trends (GT) offers insights into public interests and behaviors and holds potential for guiding public health campaigns. We evaluated trends in US searches for sunscreen, sunburn, skin cancer, and melanoma and their relationships with melanoma outcomes. Design: Google Trends was queried for US search volumes from 2004 to 2017. Time-matched search term data were correlated with melanoma outcomes data from Surveillance Epidemiology and End Results Program and United States Cancer Statistics databases (2004-2014 and 2010-2014, respectively). Setting: Users of the Google search engine in the United States. Participants: Google search engine users in the United States. This represents approximately 65% of the population. Measures: Search volumes, melanoma outcomes. Analysis: Pearson correlations between search term volumes, time, and national melanoma outcomes. Spearman correlations between state-level search data and melanoma outcomes. Results: The terms “sunscreen,” “sunburn,” “skin cancer,” and “melanoma” were all highly correlated ( P < .001), with sunscreen and sunburn having the greatest correlation ( r = 0.95). Sunscreen/sunburn searches have increased over time, but skin cancer/melanoma searches have decreased ( P < .05). Nationally, sunscreen, sunburn, and skin cancer were significantly correlated with melanoma incidence. At the state level, only sunscreen and melanoma searches were significantly correlated with melanoma incidence. Conclusions: We conclude that online skin cancer prevention campaigns should focus on the search terms “sunburn” and “sunscreen,” given the decreasing online searches for skin cancer and melanoma. This is reinforced by the finding that sunscreen searches are higher in areas with higher melanoma incidence.

scholarly journals Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study (Preprint)

2020 ◽  
Author(s):  
Joseph Younis ◽  
Harvy Freitag ◽  
Jeremy S Ruthberg ◽  
Jonathan P Romanes ◽  
Craig Nielsen ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Observational Study ◽  
Social Mobility ◽  
Reproduction Number ◽  
The United States ◽  
Google Trends ◽  
Basic Reproductive Number ◽  
Social Distancing ◽  
Google Search

BACKGROUND  The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. OBJECTIVE We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (R<sub>t</sub>) as compared to social mobility estimates reported from Google and Apple Maps. METHODS  In this observational study, the estimated R<sub>t</sub> was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of “social distancing” or “#socialdistancing” on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between R<sub>t</sub> and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to <i>P</i>&lt;.05. RESULTS Negative correlations were found between Google search interest for “social distancing” and R<sub>t</sub> in the United States (<i>P</i>&lt;.001), and between search interest and state-specific R<sub>t</sub> for 9 states with the highest COVID-19 cases (<i>P</i>&lt;.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag “#socialdistancing” and at 6 days for Twitter (<i>P</i>&lt;.001). Significant correlations between R<sub>t</sub> and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at –6 and –4 days. Meanwhile, changes in social mobility correlated best with R<sub>t</sub> at –2 days and +1 day for workplace and grocery/pharmacy, respectively. CONCLUSIONS Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with R<sub>t</sub> when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

scholarly journals Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study

10.2196/21340 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e21340 ◽  
Author(s):  
Joseph Younis ◽  
Harvy Freitag ◽  
Jeremy S Ruthberg ◽  
Jonathan P Romanes ◽  
Craig Nielsen ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Observational Study ◽  
Social Mobility ◽  
Reproduction Number ◽  
The United States ◽  
Google Trends ◽  
Basic Reproductive Number ◽  
Social Distancing ◽  
Google Search

Background  The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. Objective We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (Rt) as compared to social mobility estimates reported from Google and Apple Maps. Methods  In this observational study, the estimated Rt was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of “social distancing” or “#socialdistancing” on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between Rt and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to P<.05. Results Negative correlations were found between Google search interest for “social distancing” and Rt in the United States (P<.001), and between search interest and state-specific Rt for 9 states with the highest COVID-19 cases (P<.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag “#socialdistancing” and at 6 days for Twitter (P<.001). Significant correlations between Rt and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at –6 and –4 days. Meanwhile, changes in social mobility correlated best with Rt at –2 days and +1 day for workplace and grocery/pharmacy, respectively. Conclusions Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with Rt when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

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