ArticleScientific reports2020
COVID-19 predictability in the United States using Google Trends time series.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
65 citing papers in PubMed.
- Evaluation of Google search trends for COVID-19 vaccination administration efforts within an urban department of public health.Journal of public health research · 2026Article
- Article
- Comparison of Artificial Intelligence Tools With Human Coding for Sentiment, Topic, and Thematic Analysis Tasks of Public Health Datasets During the COVID-19 Pandemic in Australia: Case Study.Online journal of public health informatics · 2026Article
- A COVID-19 prediction Model based on symptomatology google trends and its optimization.BMC infectious diseases · 2026Article
- Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls.Cureus · 2025Review
- Google search interests and new cases of COVID-19 in Bangladesh: a vector autoregression analysis for disease surveillance.BMC research notes · 2025Article
- Leveraging Large Language Models for Infectious Disease Surveillance-Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis.Journal of medical Internet research · 2025Article
- Spatio-temporal forecasting of COVID-19 cases in the Netherlands for source and contact tracing.International journal of population data science · 2025Article
- The COVID-19 pandemic and the worldwide online interest in telepsychiatry: an infodemiological study from 2004 to 2022.Frontiers in digital health · 2025Article
- Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis.Journal of medical Internet research · 2024Observational
- The Mediating Role of Human Mobility in Temporal-Lagged Relationships Between Risk Perception and COVID-19 Dynamics in Taiwan: Statistical Modeling for Comparing the Pre-Omicron and Omicron Eras.JMIR public health and surveillance · 2024Article
- Assessing the impact of small firm dynamics on public mental health amid the pandemic in Latin America.BMC public health · 2024Article
- 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
- Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?Einstein (Sao Paulo, Brazil) · 2024Article
- Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea.F1000Research · 2024Article
- Tracking online searches for gambling activities and operators in the United Kingdom during the COVID-19 pandemic: A Google Trends™ analysis.Journal of behavioral addictions · 2023Article
- Maximum generable interest: A universal standard for Google Trends search queries.Healthcare analytics (New York, N.Y.) · 2023Article
- Engaging a national-scale cohort of smart thermometer users in participatory surveillance.NPJ digital medicine · 2023Article
- Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.JMIR public health and surveillance · 2023Article
- Fortifying Health Care Intellectual Property Transactions With Blockchain.Journal of medical Internet research · 2023Review
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.