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.

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.

scholarly journals Interest in COVID-19 in Latin America and the Caribbean: an infodemiological study using Google Trends

2021 ◽  
Vol 37 (10) ◽  
Author(s):  
Carlos Jesús Aragón-Ayala ◽  
Julissa Copa-Uscamayta ◽  
Luis Herrera ◽  
Frank Zela-Coila ◽  
Cender Udai Quispe-Juli
Keyword(s):  
Latin America ◽  
Response Time ◽  
Latin American ◽  
Google Trends ◽  
Public Attention ◽  
Search Data ◽  
The Caribbean ◽  
Global And Local ◽  
Google Search ◽  
Latin American And Caribbean

Infodemiology has been widely used to assess epidemics. In light of the recent pandemic, we use Google Search data to explore online interest about COVID-19 and related topics in 20 countries of Latin America and the Caribbean. Data from Google Trends from December 12, 2019, to April 25, 2020, regarding COVID-19 and other related topics were retrieved and correlated with official data on COVID-19 cases and with national epidemiological indicators. The Latin American and Caribbean countries with the most interest for COVID-19 were Peru (100%) and Panama (98.39%). No correlation was found between this interest and national epidemiological indicators. The global and local response time were 20.2 ± 1.2 days and 16.7 ± 15 days, respectively. The duration of public attention was 64.8 ± 12.5 days. The most popular topics related to COVID-19 were: the country’s situation (100 ± 0) and coronavirus symptoms (36.82 ± 16.16). Most countries showed a strong or moderated (r = 0.72) significant correlation between searches related to COVID-19 and daily new cases. In addition, the highest significant lag correlation was found on day 13.35 ± 5.76 (r = 0.79). Interest shown by Latin American and Caribbean countries for COVID-19 was high. The degree of online interest in a country does not clearly reflect the magnitude of their epidemiological indicators. The response time and the lag correlation were greater than in European and Asian countries. Less interest was found for preventive measures. Strong correlation between searches for COVID-19 and daily new cases suggests a predictive utility.

scholarly journals Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

2019 ◽  
Vol 16 (155) ◽  
pp. 20190080 ◽  
Author(s):  
Sasikiran Kandula ◽  
Sen Pei ◽  
Jeffrey Shaman
Keyword(s):  
Real Time ◽  
Age Groups ◽  
Forecast Accuracy ◽  
Population Level ◽  
Transmission Model ◽  
Surveillance Systems ◽  
Hospitalization Rates ◽  
The Usa ◽  
Near Term ◽  
Google Search

Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for five age groups and the overall population during five seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.

743 Keeping up with Dr. Google: When Are Patients Seeking First-aid Burn Resources?

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S204-S205
Author(s):  
David Parizh ◽  
Maleeh Effendi ◽  
Thomas L Martin
Keyword(s):  
Search Engine ◽  
Hospital Setting ◽  
First Aid ◽  
Third Party ◽  
Google Trends ◽  
Burn Treatment ◽  
Burn Care ◽  
Search Data ◽  
Search Numbers ◽  
Google Search

Abstract Introduction Treating burns is a relatively common occurrence in American Emergency Departments occurring an estimated 486,000 times per year. In the digital era, patients feel increasingly empowered to seek out medical resources independently. The true number of people sustaining an injury and treating themselves at home or outside of the hospital setting is difficult to quantify. However, we can see when patients were searching for first-aid burn resources on the world’s most powerful and popular search engine - Google. We hypothesized that there would be a correlation between patient’s searching for burn care resources online and burn admissions. Methods We used Keywords Everywhere a browser add-on for Google Chrome to cross check various phrases and words that Americans might search for to find information on how to treat a burn. “Burn treatment” was found to be the most commonly searched phrase and this was verified using Google Trends. Google Trends dose not give raw search numbers. However, it expresses the search frequency for a term relative to how frequently that term was sought out during a specified time period. We pulled search data for each successive year back till 2006 the earliest year for which complete data was available. We were then able to overlay this data on a year to year basis and thus view when information about treating burns was the most sought out. Results A clear increase in the frequency of searches for burn treatment can be seen around the summer months, peaking in the week surrounding the 4th of July. Further data comparing this trend to burn admissions is forthcoming as data is being solicited. Conclusions Americans are searching for more resources regarding burn injuries in the summer months; and especially in the days surrounding the fourth of July. We are excited to correlate this data to burn admissions. If there is an inverse relationship between admissions during the summer months and number of inquiries made via Google for acute burn care, this may suggest that many of the burns are minor. Thus, being treated through our clinics or through third-party providers. Alternatively, the patients may be treating themselves using internet resources. If this proves to be the case, there may be an opportunity to enrich online resources for our patients. Applicability of Research to Practice Once the data processing is complete, there will be an indication if the number of people seeking out resources via Google Search Engine correlates with out burn admissions. If not, this may be an opportunity for improvement to enrich burn first-aid resources available online.

Anxious? Just Google It: Social Ecological Factors of Internet Search Records on Anxiety

2018 ◽  
Author(s):  
Takeshi Hamamura ◽  
Christian Shaunlyn Chan
Keyword(s):  
Social Science Research ◽  
Ecological Factors ◽  
Population Level ◽  
Science Research ◽  
Psychological Research ◽  
Self Report ◽  
Internet Search ◽  
Major Disaster ◽  
Search Data ◽  
Google Search

Records of Internet search are increasingly used in social science research. Three studies reported here tested (a) whether population-level anxiety is reflected in Internet search data and (b) the socio-ecological and cultural factors of anxiety. Using data from Japan, we found that the Google search rates of anxiety are associated with self-report anxiety (Study 1), and that the search rates increased following a major disaster (Study 2). These findings suggest that anxiety is searched more often on the Internet when and where people are feeling anxious. However, while search rates of anxiety increased since 2010, there was no sign of worsening anxiety among Japanese in two large national data on self-report anxiety (Study 1). Study 3 used search data to examine an anxiety-related cultural phenomenon. Consistent with a lay belief that is rarely empirically examined, we found that anxiety among Japanese increases in spring when millions in the country make school and career transitions. This pattern was somewhat more pronounced in large cities and was not evident in other negative emotions examined. Together, these findings add to psychologists’ understanding of anxiety particularly its vulnerability to environmental threat and social disconnection. These findings also demonstrate the potential of Internet search data in advancing psychological research, particularly in examining mental processes’ socio-ecological, cultural, and temporal factors.

scholarly journals Real-time characterization of the molecular epidemiology of an influenza pandemic

2013 ◽  
Vol 9 (5) ◽  
pp. 20130331 ◽  
Author(s):  
J. Hedge ◽  
S. J. Lycett ◽  
A. Rambaut
Keyword(s):  
Real Time ◽  
Influenza A ◽  
Sequence Data ◽  
Influenza Pandemic ◽  
H1n1 Influenza ◽  
Accurate Estimation ◽  
Time Estimates ◽  
Dataset Size ◽  
2009 H1n1

Early characterization of the epidemiology and evolution of a pandemic is essential for determining the most appropriate interventions. During the 2009 H1N1 influenza A pandemic, public databases facilitated widespread sharing of genetic sequence data from the outset. We use Bayesian phylogenetics to simulate real-time estimates of the evolutionary rate, date of emergence and intrinsic growth rate ( r 0 ) of the pandemic from whole-genome sequences. We investigate the effects of temporal range of sampling and dataset size on the precision and accuracy of parameter estimation. Parameters can be accurately estimated as early as two months after the first reported case, from 100 genomes and the choice of growth model is important for accurate estimation of r 0 . This demonstrates the utility of simple coalescent models to rapidly inform intervention strategies during a pandemic.

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 Modeling Zika Virus Spread in Colombia Using Google Search Queries and Logistic Power Models

2018 ◽  
Author(s):  
Mekenna Brown ◽  
Christopher Cain ◽  
James Whitfield ◽  
Edwin Ding ◽  
Sara Y Del Valle ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
Google Trends ◽  
Logistic Growth ◽  
Data Sets ◽  
Surveillance Systems ◽  
Public Health Agencies ◽  
Power Models ◽  
Clinical Surveillance ◽  
Search Data ◽  
Google Search

AbstractPublic health agencies generally have a small window to respond to burgeoning disease outbreaks in order to mitigate the potential impact. There has been significant interest in developing forecasting models that can predict how and where a disease will spread. However, since clinical surveillance systems typically publish data with a lag of two or more weeks, there is a need for complimentary data streams that can close this gap. We examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread. We calculated the correlation and the time delay between the reported case data and the Google Trends data using variations of the logistic growth model, and showed that the data sets were systematically offset from each other, implying a lead time in the Google Trends data. Our study showed how Internet data can potentially complement clinical surveillance data and may be used as an effective early detection tool for disease outbreaks.

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