ArticleCommunications medicine2025
Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Mapping the Surveillance Data Infrastructure in the U.S. for SARS-CoV-2, Influenza, and Respiratory Syncytial Virus.Current epidemiology reports · 2026Review
- Digital Trends in the Italian Beer Market: A Time-Series and Search Engine Optimisation Analysis of Gluten-Free and Low/No-Alcohol Beers.Foods (Basel, Switzerland) · 2025Article
- Digital Insights into Workplace Breastfeeding in Indonesia: A Google Trends Analysis of Barriers and Opportunities.Nutrients · 2025Article
- A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones.Entropy (Basel, Switzerland) · 2025Article
- Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence.Communications medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundLong COVID, the persistent illness following COVID-19 infection, has emerged as a major public health concern since the outbreak of the pandemic. Effective disease surveillance is crucial for policymaking and resource allocation.
methodsWe investigated the potential of utilizing Google Trends data to enhance long COVID symptoms surveillance. Though Google Trends provides freely available search popularity data, limitations in data normalization and retrieval restrictions have hindered its predictive capabilities. In our study, we carefully selected 33 search terms and 20 related topics from the long COVID symptoms list provided by the Centers for Disease Control and Prevention and the database "scite", and calculated their merged search volumes from Google Trends data using our developed statistical method for analysis.
resultsWe identify four related topics (ageusia, anosmia, chest pain, and headaches) that consistently exhibit increased search popularity before that of "long COVID." Additionally, nine related topics (aching muscle pain, anxiety, chest pain, clouding of consciousness, dizziness, fatigue, myalgia, shortness of breath, and hypochondriasis) show increased search popularity following that of "long COVID." We demonstrate that the merged search volume (MSV), derived from the relative search volume data downloaded from Google, can be used to forecast the prevalence of long COVID in a prediction study, supporting the use of the methodology in risk management regarding the prevalence of long COVID.
conclusionsBy utilizing a comprehensive list of search terms and sophisticated statistical analytics, our study contributes to exploring the potential of Google Trends data for forecasting and monitoring long COVID prevalence. These findings and methodologies can be used as prior knowledge to inform future infodemiological and epidemiological investigations.
Identifiers
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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.