scholarly journals Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260931
Author(s):  
Catherine Gimbrone ◽  
Caroline Rutherford ◽  
Sasikiran Kandula ◽  
Gonzalo Martínez-Alés ◽  
Jeffrey Shaman ◽  
...  
Keyword(s):  
Mental Health ◽  
Real Time ◽  
Economic Stress ◽  
Warning Signs ◽  
P Value ◽  
Volume Data ◽  
Search Volume ◽  
Search Data ◽  
Google Search ◽  
At Home

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic’s social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.

Discovering business information from search engine query data

2014 ◽  
Vol 38 (4) ◽  
pp. 562-574 ◽  
Author(s):  
Liwen Vaughan
Keyword(s):  
Search Engine ◽  
Business Performance ◽  
Google Trends ◽  
Volume Data ◽  
Content Type ◽  
Search Volume ◽  
Business Information ◽  
Position Data ◽  
Search Data ◽  
One Year

Purpose – The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data. Design/methodology/approach – The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches. Findings – The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose. Research limitations/implications – The study is limited to only one country and to one year of data. Practical implications – Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data. Originality/value – This is the first study to establish a relationship between search engine query data and business performance and position data.

scholarly journals Internet search query data improve forecasts of daily emergency department volume

2019 ◽  
Vol 26 (12) ◽  
pp. 1574-1583 ◽  
Author(s):  
Sam Tideman ◽  
Mauricio Santillana ◽  
Jonathan Bickel ◽  
Ben Reis
Keyword(s):  
Predictive Power ◽  
Linear Models ◽  
Data Sources ◽  
Weather Data ◽  
Volume Data ◽  
Internet Search ◽  
Search Activity ◽  
Search Volume ◽  
Day Of The Week ◽  
Google Search

Abstract Objective Emergency departments (EDs) are increasingly overcrowded. Forecasting patient visit volume is challenging. Reliable and accurate forecasting strategies may help improve resource allocation and mitigate the effects of overcrowding. Patterns related to weather, day of the week, season, and holidays have been previously used to forecast ED visits. Internet search activity has proven useful for predicting disease trends and offers a new opportunity to improve ED visit forecasting. This study tests whether Google search data and relevant statistical methods can improve the accuracy of ED volume forecasting compared with traditional data sources. Materials and Methods Seven years of historical daily ED arrivals were collected from Boston Children’s Hospital. We used data from the public school calendar, National Oceanic and Atmospheric Administration, and Google Trends. Multiple linear models using LASSO (least absolute shrinkage and selection operator) for variable selection were created. The models were trained on 5 years of data and out-of-sample accuracy was judged using multiple error metrics on the final 2 years. Results All data sources added complementary predictive power. Our baseline day-of-the-week model recorded average percent errors of 10.99%. Autoregressive terms, calendar and weather data reduced errors to 7.71%. Search volume data reduced errors to 7.58% theoretically preventing 4 improperly staffed days. Discussion The predictive power provided by the search volume data may stem from the ability to capture population-level interaction with events, such as winter storms and infectious diseases, that traditional data sources alone miss. Conclusions This study demonstrates that search volume data can meaningfully improve forecasting of ED visit volume and could help improve quality and reduce cost.

Seasonality and global public interest in psoriasis: an infodemiology study

2019 ◽  
Vol 96 (1133) ◽  
pp. 139-143 ◽  
Author(s):  
Qian Wu ◽  
Zhiwei Xu ◽  
Yi-Lin Dan ◽  
Chan-Na Zhao ◽  
Yan-Mei Mao ◽  
...  
Keyword(s):  
New Zealand ◽  
Public Interest ◽  
Seasonal Pattern ◽  
Late Summer ◽  
Early Spring ◽  
Late Winter ◽  
Search Volume ◽  
Early Autumn ◽  
Search Data ◽  
Google Search

ObjectiveAlthough patients with psoriasis frequently report seasonal changes in their symptoms, the seasonality of psoriasis has rarely been explored. This study aims to investigate the seasonal pattern of and global public interest in psoriasis using Google search data.MethodsInternet search data were collected from Google Trends. Data on the relative search volume (RSV) from January 2004 to December 2018 were retrieved using the term psoriasis. Cosinor analyses were conducted to examine the seasonality of psoriasis using data from two southern hemisphere countries (Australia and New Zealand) and four northern hemisphere countries (USA, Canada, UK and Ireland).ResultsOverall, searches for psoriasis steadily decreased between 2004 and 2010, and then rose from 2011 to 2018. On cosinor analyses, RSV of ‘psoriasis’ displayed a significant seasonal variation worldwide (p<0.025). Further analyses confirmed the seasonality of psoriasis-related RSV in Australia, New Zealand, USA, Canada, UK and Ireland (p<0.025 for all), with peaks in the late winter/early spring months and troughs in the late summer/early autumn months. The top 11 rising topics were calcipotriol/betamethasone dipropionate, ustekinumab, apremilast, shampoo, eczema, guttate psoriasis, seborrhoeic dermatitis, dermatitis, psoriatic arthritis, atopic dermatitis and arthritis.ConclusionThere was a significant seasonal pattern for psoriasis, with peaks in the late winter/early spring and troughs in the late summer/early autumn. Further studies are warranted to confirm the seasonal pattern of psoriasis using clinical data and to explore the underlying mechanisms.

scholarly journals Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework (Preprint)

2019 ◽  
Author(s):  
Anne Zepecki ◽  
Sylvia Guendelman ◽  
John DeNero ◽  
Ndola Prata
Keyword(s):  
Real Time ◽  
Birth Control ◽  
Google Trends ◽  
Search Term ◽  
Health Trends ◽  
Search Queries ◽  
Search Volume ◽  
Application Programming ◽  
Relative Search Volume ◽  
Google Search

BACKGROUND Individuals are increasingly turning to search engines like Google to obtain health information and access resources. Analysis of Google search queries offers a novel approach, which is part of the methodological toolkit for infodemiology or infoveillance researchers, to understanding population health concerns and needs in real time or near-real time. While searches predominantly have been examined with the Google Trends website tool, newer application programming interfaces (APIs) are now available to academics to draw a richer landscape of searches. These APIs allow users to write code in languages like Python to retrieve sample data directly from Google servers. OBJECTIVE The purpose of this paper is to describe a novel protocol to determine the top queries, volume of queries, and the top sites reached by a population searching on the web for a specific health term. The protocol retrieves Google search data obtained from three Google APIs: Google Trends, Google Health Trends (also referred to as Flu Trends), and Google Custom Search. METHODS Our protocol consisted of four steps: (1) developing a master list of top search queries for an initial search term using Google Trends, (2) gathering information on relative search volume using Google Health Trends, (3) determining the most popular sites using Google Custom Search, and (4) calculating estimated total search volume. We tested the protocol following key procedures at each step and verified its usefulness by examining search traffic on <i>birth control</i> in 2017 in the United States. Two separate programmers working independently achieved similar results with insignificant variation due to sample variability. RESULTS We successfully tested the methodology on the initial search term <i>birth control</i>. We identified top search queries for <i>birth control</i>, of which <i>birth control pill</i> was the most popular and obtained the relative and estimated total search volume for the top queries: relative search volume was 0.54 for the pill, corresponding to an estimated 9.3-10.7 million searches. We used the estimates of the proportion of search activity for the top queries to arrive at a generated list of the most popular websites: for the pill, the Planned Parenthood website was the top site. CONCLUSIONS The proposed methodological framework demonstrates how to retrieve Google query data from multiple Google APIs and provides thorough documentation required to systematically identify search queries and websites, as well as estimate relative and total search volume of queries in real time or near-real time in specific locations and time periods. Although the protocol needs further testing, it allows researchers to replicate the steps and shows promise in advancing our understanding of population-level health concerns. INTERNATIONAL REGISTERED REPORT RR1-10.2196/16543

scholarly journals What Do Germans Want to Know About Skin Cancer? A Nationwide Google Search Analysis From 2013 to 2017 (Preprint)

2018 ◽  
Author(s):  
Stefanie Seidl ◽  
Barbara Schuster ◽  
Melvin Rüth ◽  
Tilo Biedermann ◽  
Alexander Zink
Keyword(s):  
Skin Cancer ◽  
Temporal Trends ◽  
Nonmelanoma Skin Cancer ◽  
German Population ◽  
White Skin ◽  
Awareness Campaigns ◽  
Search Terms ◽  
Search Volume ◽  
Search Data ◽  
Google Search

BACKGROUND Experts worldwide agree that skin cancer is a global health issue, but only a few studies have reported on world populations’ interest in skin cancer. Internet search data can reflect the interest of a population in different topics and thereby identify what the population wants to know. OBJECTIVE Our aim was to assess the interest of the German population in nonmelanoma skin cancer and melanoma. METHODS Google AdWords Keyword Planner was used to identify search terms related to nonmelanoma skin cancer and melanoma in Germany from November 2013 to October 2017. The identified search terms were assessed descriptively using SPSS version 24.0. In addition, the search terms were qualitatively categorized. RESULTS A total of 646 skin cancer-related search terms were identified with 19,849,230 Google searches in the period under review. The search terms with the highest search volume were “skin cancer” (n=2,388,500, 12.03%), “white skin cancer” (n=2,056,900, 10.36%), “basalioma” (n=907,000, 4.57%), and “melanoma” (n=717,800, 3.62%). The most searched localizations of nonmelanoma skin cancer were “nose” (n=93,370, 38.99%) and “face” (n=53,270, 22.24%), and the most searched of melanoma were “nails” (n=46,270, 70.61%) and “eye” (n=10,480, 15.99%). The skin cancer‒related category with the highest search volume was “forms of skin cancer” (n=10,162,540, 23.28%) followed by “skin alterations” (n=4,962,020, 11.36%). CONCLUSIONS Our study provides insight into terms and fields of interest related to skin cancer relevant to the German population. Furthermore, temporal trends and courses are shown. This information could aid in the development and implementation of effective and sustainable awareness campaigns by developing information sources targeted to the population’s broad interest or by implementing new Internet campaigns.

scholarly journals COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study (Preprint)

2020 ◽  
Author(s):  
Alberto Jimenez Jimenez ◽  
Rosa M Estevez-Reboredo ◽  
Miguel A Santed ◽  
Victoria Ramos
Keyword(s):  
Real Time ◽  
Influenza A ◽  
Time Lag ◽  
Population Level ◽  
H1n1 Influenza ◽  
Google Trends ◽  
Small Contribution ◽  
Search Patterns ◽  
Search Data ◽  
Google Search

BACKGROUND COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. OBJECTIVE In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. METHODS We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. RESULTS In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. CONCLUSIONS During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.

Gauging public awareness and interest related to pancreatic cancer: An analysis of Google search volumes.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 250-250
Author(s):  
Dhruvika Mukhija ◽  
Alok A. Khorana ◽  
Davendra Sohal
Keyword(s):  
Pancreatic Cancer ◽  
Colon Cancer ◽  
Public Awareness ◽  
P Value ◽  
Internet Search ◽  
Awareness Campaigns ◽  
Search Terms ◽  
Search Volume ◽  
News Events ◽  
Google Search

250 Background: Over the last 2 decades, the internet has become a major source of medical information. Infoveillance, i.e., public health surveillance using online content analysis has become a powerful tool and internet search activity has been used as a surrogate to gauge public awareness and interest for particular diseases. We aimed to evaluate the search volume for pancreatic cancer (PC), using colon cancer (CC), as a comparator, using data from a popular search engine. Methods: Using Google Trends, a public web facility of Google Inc., based on Google Search, we compared the relative frequency of search terms ‘pancreatic cancer’ and ‘colon cancer’ between 1st January 2004 and 31st August 2017 (n = 164 months). The program assigns a reference value of 100 for the point of maximum popularity from among all the search terms during the search period and provides comparative monthly scores, which we termed relative interest scores (RIS). The RIS for each cancer was then adjusted for incidence (i.e., 53,070 for PC and 95,270 for CC, based on 2016 data), calculated per 10,000 patients and termed ‘i-RIS’. A p-value of < 0.05 was considered significant. Results: For the entire duration, the maximum popularity (RIS = 100) corresponded to a point in March 2008 for PC, likely related to the diagnosis of a famous celebrity during that month. Similar but smaller surges in RIS were observed for other significant news events related to PC during other months (January 2009, October 2009 and October 2011). Overall, the mean (±S.D) RIS for PC and CC were 32.52±8.98 and 50.18±6.44, respectively (p < 0.001). However, the i-RIS was somewhat higher for PC (6.12±1.69) as compared with CC (5.26±0.67) (p < 0.001). Conclusions: Internet search data can provide estimates of public awareness and interest related to cancer. For PC, incidence-adjusted search volumes show spikes in search volumes related to major news events, providing internal validation of these results. Generating news items and promotion by celebrities may play a significant role in the success of cancer awareness campaigns.

scholarly journals Monitoring Interest in Herpes Zoster Vaccination: Analysis of Google Search Data (Preprint)

2018 ◽  
Author(s):  
Elyse J Berlinberg ◽  
Michael S Deiner ◽  
Travis C Porco ◽  
Nisha R Acharya
Keyword(s):  
Public Health ◽  
Herpes Zoster ◽  
Vaccine Coverage ◽  
Application Programming Interface ◽  
The United States ◽  
Live Attenuated Vaccine ◽  
Attenuated Vaccine ◽  
Search Volume ◽  
Search Data ◽  
Google Search

BACKGROUND A new recombinant subunit vaccine for herpes zoster (HZ or shingles) was approved by the United States Food and Drug Administration on October 20, 2017 and is expected to replace the previous live attenuated vaccine. There have been low coverage rates with the live attenuated vaccine (Zostavax), ranging from 12-32% of eligible patients receiving the HZ vaccine. OBJECTIVE This study aimed to provide insight into trends and potential reasons for interest in HZ vaccination. METHODS Internet search data were queried from the Google Health application programming interface from 2004-2017. Seasonality of normalized search volume was analyzed using wavelets and Fisher’s g test. RESULTS The search terms “shingles vaccine,” “zoster vaccine,” and “zostavax” all exhibited significant periodicity in the fall months (P<.001), with sharp increases after recommendations for vaccination by public health-related organizations. Although the terms “shingles blisters,” “shingles itch,” “shingles rash,” “skin rash,” and “shingles medicine” exhibited statistically significant periodicities with a seasonal peak in the summer (P<.001), the terms “shingles contagious,” “shingles pain,” “shingles treatment,” and “shingles symptoms” did not reveal an annual trend. CONCLUSIONS There may be increased interest in HZ vaccination during the fall and after public health organization recommendations are broadcast. This finding points to the possibility that increased awareness of the vaccine through public health announcements could be evaluated as a potential intervention for increasing vaccine coverage.

scholarly journals COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study

10.2196/23518 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23518 ◽  
Author(s):  
Alberto Jimenez Jimenez ◽  
Rosa M Estevez-Reboredo ◽  
Miguel A Santed ◽  
Victoria Ramos
Keyword(s):  
Real Time ◽  
Influenza A ◽  
Time Lag ◽  
Population Level ◽  
H1n1 Influenza ◽  
Google Trends ◽  
Small Contribution ◽  
Search Patterns ◽  
Search Data ◽  
Google Search

Background COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. Objective In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. Methods We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. Results In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. Conclusions During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.

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