ArticleBMC medical research methodology2021
Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association between public attention and monkeypox epidemic: A global lag-correlation analysis.Journal of medical virology · 2023Pooled it
- 8. Guard against statistical fallacies in medical research.Journal of postgraduate medicine · 2026Article
- Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence.Communications medicine · 2025Article
- Comment on: search traffic for inflatable penile prosthesis increased following the COVID-19 pandemic in the United States: a Google Trends analysis.International journal of impotence research · 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 Interest in Betting, Smoking, Alcohol, and Drugs in Malawi: Changing Trends between 2012-2022.Malawi medical journal : the journal of Medical Association of Malawi · 2024Article
- Mining Google Trends data for nowcasting and forecasting colorectal cancer (CRC) prevalence.PeerJ. Computer science · 2023Article
- Googling Insomnia, Light, Metabolism, and Circadian: A Population Interest Simple Report.Brain sciences · 2022Article
- Robust time-series analysis of the effects of environmental factors on the CoViD-19 pandemic in the area of Milan (Italy) in the years 2020-21.Hygiene and environmental health advances · 2022Article
- Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis.JMIR dermatology · 2022Article
- Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022Review
- Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation.JMIR medical informatics · 2022Article
- Geographic social inequalities in information-seeking response to the COVID-19 pandemic in China: longitudinal analysis of Baidu Index.Scientific reports · 2022Article
- A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study.Journal of medical Internet research · 2022Observational
- A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy.BMC medical research methodology · 2022Article
- Online public attention toward allergic rhinitis in Wuhan, China: Infodemiology study using Baidu index and meteorological data.Frontiers in public health · 2022Article
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- Search queries related to COVID-19 based on keyword extraction.Procedia computer science · 2022Article
- Analyzing Citizens' and Health Care Professionals' Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs.JMIR public health and surveillance · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGoogle Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.
methodsWe extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.
resultsOur Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by Pearson or Spearman's rank correlation-based approach. "Sense of smell" and "loss of smell" were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan).
conclusionsOur results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.
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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 Socratic graph.