ArticleJournal of medical Internet research2020
COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Global information-seeking behavior of air pollution and cardiovascular disease: insights from google trends analysis.Frontiers in epidemiology · 2026Article
- On the importance of communication, advocacy, and information accuracy in destigmatizing mpox disease.Scientific reports · 2025Article
- Deep recurrent models for forecasting infectious diseases.Frontiers in public health · 2025Article
- Public and Research Interest in Telemedicine From 2017 to 2022: Infodemiology Study of Google Trends Data and Bibliometric Analysis of Scientific Literature.Journal of medical Internet research · 2024Article
- Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model.Frontiers in public health · 2024Article
- The evolution of the COVID-19 pandemic through the lens of google searches.Scientific reports · 2023Article
- The lead time and geographical variations of Baidu Search Index in the early warning of COVID-19.Scientific reports · 2023Article
- Did online information seeking for depression increase during COVID-19 lockdown times? A google trend analysis on data from Germany and the UK.Journal of affective disorders reports · 2023Article
- Hangover-Related Internet Searches Before and During the COVID-19 Pandemic in England: Observational Study.JMIR formative research · 2023Article
- Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.Frontiers in public health · 2023Article
- Public interest in different types of masks and its relationship with pandemic and policy measures during the COVID-19 pandemic: a study using Google Trends data.Frontiers in public health · 2023Article
- State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data.JMIR formative research · 2022Article
- Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022Review
- COVID-19 forecasts using Internet search information in the United States.Scientific reports · 2022Article
- Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence.Infectious Disease Modelling · 2022Article
- Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis.Frontiers in public health · 2022Article
- Article
- Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms.Communications medicine · 2022Article
- Google Trends for Pain Search Terms in the World's Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study.Journal of medical Internet research · 2021Article
- Prevalence and Perception Among Saudi Arabian Population About Resharing of Information on Social Media Regarding Natural Remedies as Protective Measures Against COVID-19.International journal of general medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-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.
objectiveIn 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.
methodsWe 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.
resultsIn 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.
conclusionsDuring 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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