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.

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.

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.

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.

Where and When Can We Use Google Trends to Measure Issue Salience?

2013 ◽  
Vol 46 (02) ◽  
pp. 280-290 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Google Trends ◽  
Issue Salience ◽  
Social Scientists ◽  
Download Time ◽  
The World ◽  
Search Data ◽  
Data Source ◽  
Almost All ◽  
Google Search ◽  
Over Time

Google search data have several major advantages over traditional survey data. First, the high costs of running frequent surveys mean that most survey questions are only asked occasionally making comparisons over time difficult. By contrast, Google Trends provides information on search trends measured weekly. Second, there are many countries where surveys are only conducted sporadically, whereas Google search data are available anywhere in the world where sufficient numbers of people use its search engine. The Google Trends website allows researchers to download data for almost all countries at no cost and to download time series of any search term's popularity over time (provided enough people have searched for it). For these reasons, Google Trends is an attractive data source for social scientists.

scholarly journals Forecasting influenza-like illness trends in Cameroon using Google Search Data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elaine O. Nsoesie ◽  
Olubusola Oladeji ◽  
Aristide S. Abah Abah ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
Mean Squared Error ◽  
Sub Saharan Africa ◽  
Digital Data ◽  
Support Vector ◽  
Surveillance Systems ◽  
African Countries ◽  
Influenza Like Illness ◽  
Search Data ◽  
Sub Saharan ◽  
Google Search

AbstractAlthough acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

scholarly journals Nowcasting Influenza-like Illness Trends in Cameroon

2020 ◽  
Author(s):  
Elaine O. Nsoesie ◽  
Olubusola Oladeji ◽  
Aristide S. Abah Abah ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
Disease Surveillance ◽  
Respiratory Infections ◽  
Sub Saharan Africa ◽  
Digital Data ◽  
Surveillance Systems ◽  
African Countries ◽  
Influenza Like Illness ◽  
Search Data ◽  
Sub Saharan ◽  
Google Search

ABSTRACTAlthough acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied statistical and machine learning models to forecast 2012 to 2018 trends in ILI cases reported by the Cameroon Ministry of Health (MOH), using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The variance explained by the models based on Google search data were 87.7%, 79.1% and 52.0% for the whole country, the Littoral and Centre regions respectively. Our study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and demonstrates the potential usefulness of search data for monitoring ILI in sub-Saharan African countries.

scholarly journals What value can Google search data add to existing syndromic surveillance systems?

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Helen K. Green ◽  
Obaghe Edeghere ◽  
Alex Elliot ◽  
Ingemar Cox ◽  
Rachel McKendry ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Added Value ◽  
Surveillance Systems ◽  
Search Term ◽  
Public Health Importance ◽  
Health Events ◽  
Search Terms ◽  
Search Data ◽  
Google Search

ObjectiveTo carry out an observational study to explore what added value Google search data can provide to existing routine syndromic surveillance systems in England for a range of conditions of public health importance and summarise lessons learned for other countries.IntroductionGlobally, there have been various studies assessing trends in Google search terms in the context of public health surveillance1. However, there has been a predominant focus on individual health outcomes such as influenza, with limited evidence on the added value and practical impact on public health action for a range of diseases and conditions routinely monitored by existing surveillance programmes. A proposed advantage is improved timeliness relative to established surveillance systems. However, these studies did not compare performance against other syndromic data sources, which are often monitored daily and already offer early warning over traditional surveillance methods. Google search data could also potentially contribute to assessing the wider population health impact of public health events by supporting estimation of the proportion of the population who are symptomatic but may not present to healthcare services.MethodsWe sought to determine the added public health utility of Google search data alongside established syndromic surveillance systems in England2 for a range of conditions of public health importance, including allergic rhinitis, scarlet fever, bronchitis, pertussis, measles, rotavirus and the health impact of heatwaves. Google search term selection was based on diagnostic and clinical codes underlying the syndromic indicators, with Google Trends3 used to identify additional related internet search terms. Daily data was extracted from syndromic surveillance systems2 and from the Google Health Trends Application Programming Interface (API) from 2012 to 2017 and a retrospective daily analysis undertaken during pre-identified public health events to identify a) whether signals were detected during these events and b) assess the correlation with analogous syndromic surveillance indicators through calculation of Spearman correlation coefficients and lag assessment to determine timeliness.ResultsWe detected increases in Google search term frequency during public health events of interest. Good correlation was seen with comparable syndromic surveillance indicators on a daily timescale for several health outcomes, including the search terms hayfever, scarlet fever, bronchiolitis and heatstroke. Weaker correlation was seen for conditions which occur in small numbers and are vaccine preventable such as measles and pertussis. Lag analysis showed similar timeliness between daily syndromic and Google data, suggesting that, overall, Google data did not provide an earlier or delayed signal compared to syndromic surveillance indicators in England.ConclusionsTo the best of our knowledge this is the first time trends in Google search data have been compared against syndromic data for a range of public health conditions in England. These findings demonstrate the potential utility of internet search query data in conjunction with existing systems in England, with syndromic surveillance data found to be as timely as Google data. These findings also have important implications for countries where there are no such healthcare-based syndromic surveillance systems in place. Factors to consider with analyses of Google search trend data in the context of disease surveillance have been highlighted, including the choice of search terms and interpretation of the reasons behind searching the internet.References1Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, Murugiah K. The use of google trends in health care research: a systematic review. PLoS One. 2014 Oct 22;9(10):e109583.2Public Health England. Syndromic surveillance: systems and analyses. 2017. Available online: https://www.gov.uk/government/collections/syndromic-surveillance-systems-and-analyses3Google. 2017. Google Trends. Available online:https://trends.google.com/trends/

scholarly journals The Pace and Pulse of the Fight against Coronavirus across the US, A Google Trends Approach (Preprint)

2020 ◽  
Author(s):  
Tichakunda Mangono ◽  
Peter Smittenaar ◽  
Yael Caplan ◽  
Vincent Huang ◽  
Staci Sutermaster ◽  
...  
Keyword(s):  
Health Behaviors ◽  
Real Time ◽  
Information Seeking ◽  
National Level ◽  
Google Trends ◽  
Urgent Care ◽  
Data Sets ◽  
Single Issue ◽  
The Us ◽  
Search Data

BACKGROUND The coronavirus pandemic is impacting our lives at unprecedented speed and scale - including how we eat and work, what we worry about, how much we move, and our ability to earn. Traditional surveys in the area of public health can be expensive, time-consuming, and rapidly go out of date. Analyzing big data sets (such as electronic patient records, surveillance systems) is very complex. However, Google Trends is an alternative approach which has been used before to analyze health behaviors, but most research on COVID-19 using this data, so far, looks at a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the US. OBJECTIVE We use Google Trends to provide both insights into, and potential indicators of, important changes in information-seeking patterns during pandemics like COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Does search data correlate with – and even precede – real-life events? METHODS We analyzed searches on 39 terms related to COVID-19, falling into six themes: Social & Travel; Care Seeking; Government Programs; Health Programs; News & Influence; Outlook & Concerns. We generated data sets at the national level (covering Jan 1, 2016 – April 15, 2020) and state level (covering Jan 1, 2020 – April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states during March 1st to April 15th, 2020; and Principal Component Analyses (PCA) to extract search patterns across states. RESULTS Data showed high demand for information corresponded with increasing searches for “coronavirus” linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often happened well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on coronavirus care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointment, health insurance/ Medicare/ Medicaid. Finally, concerns vary across the country - some search terms were more popular in some regions than in others. CONCLUSIONS COVID-19 is unlikely to be the last pandemic disease the US faces. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions (NPIs) and recommend the development of a real-time dashboard as a decision-making tool. CLINICALTRIAL N/A

Prediction of norovirus using Internet search data in the US (Preprint)

2020 ◽  
Author(s):  
Kai Yuan ◽  
Guangrui Huang ◽  
Haixu Jiang ◽  
Wenbin Liu ◽  
Ting Wang ◽  
...  
Keyword(s):  
New York ◽  
Disease Outbreaks ◽  
Temporal Correlation ◽  
Google Trends ◽  
Watery Diarrhea ◽  
Norwalk Virus ◽  
Internet Search ◽  
Search Terms ◽  
Cross Correlation Analysis ◽  
Google Search

BACKGROUND Norovirus is a contagious disease leading to vomiting and diarrhea. The transmission of norovirus spreads quickly and easily in various ways. Because effective methods to prevent or treat norovirus have not been discovered, it is important to rapidly recognize and report norovirus outbreaks in the early phase. Internet search has been a useful method for people to access information immediately. With the precise record of Internet search trends, Internet search has been a useful tool to manifest infectious disease outbreaks. OBJECTIVE In this study, we tried to discover the correlation between Internet search terms and norovirus infection. METHODS The Internet search trend data of norovirus were obtained from Google Trends. We used cross-correlation analysis to discover the temporal correlation between norovirus and other terms. We also used multiple linear regression with the stepwise method to recognize the most important predictors of Internet search trends and norovirus. In addition, we evaluated the temporal correlation between actual norovirus cases and Internet search terms in New York, California, and USA. RESULTS Some Google search terms such as gastroenteritis, vomiting, and watery diarrhea were coincided with norovirus Google Trends. Some Google search terms such as contagious, Norwalk virus, travel presented earlier than norovirus Google Trends. Some Google search terms such as dehydration, bar, and restaurant presented several months later than norovirus Google Trends. We found that the symptoms of gastroenteritis, including vomiting and watery diarrhea, were important factors that were significantly correlated with norovirus Google Trends. In actual norovirus cases of New York, California, and USA, some Google search terms presented coincided, earlier, or later than actual norovirus cases. CONCLUSIONS Our study provides novel strategy-based Internet search evidence regarding the epidemiology of norovirus.

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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