A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

Wang, Yining; Shi, Wenbin; Sun, Yueqiang; Yeh, Chien-Hung

doi:10.1109/tcss.2023.3255256

IEEE Trans. Comput. Soc. Syst.

2024

DOI: 10.1109/tcss.2023.3255256

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A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

Yining Wang

Wenbin Shi

Yueqiang Sun

et al.

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2025

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Google Trends applications for COVID-19 pandemic: A bibliometric analysis

Li,

Zhang,

et al. 2025

DIGITAL HEALTH

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Introduction COVID-19 is one of the most severe global health events in recent years. Google Trends provides a comprehensive analysis of the search frequency for specific terms on Google, reflecting the public's areas of interest. As of now, there has been no bibliometric study on COVID-19 and Google Trends. Therefore, the aim of this study is to perform a comprehensive bibliometric analysis of existing Google Trends research related to COVID-19. Methods We retrieved 467 records from the Web of Science™ Core Collection, covering the period from January 1, 2020, to December 31, 2023. We then conducted scientific metric analyses using CiteSpace, VOSviewer, and the Bibliometrix package in R-software to explore the temporal and spatial distribution, author distribution, thematic categories, references, and keywords related to these records. Results A total of 467 valid records, comprising 418 articles and 49 reviews, were collected for analysis. Over the 4 years, the highest number of publications occurred in 2021. The United States had the most published papers, followed by China. Notably, the United States and China had the closest collaborative relationship. Harvard University ranked as the institution with the highest number of published papers. However, there appeared to be a lack of collaboration between institutions. The research hotspots related to COVID-19 in Google Trends encompassed “outbreak,” “epidemic,” “air pollution,” “internet,” “time series,” and “public interest.” Conclusion This study provides a valuable overview of the directions in which Google Trends is being utilized for studying infectious diseases, particularly COVID-19.

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Google Trends applications for COVID-19 pandemic: A bibliometric analysis

Li,

Zhang,

et al. 2025

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

Cited by 1 publication

References 40 publications

Google Trends applications for COVID-19 pandemic: A bibliometric analysis

Google Trends applications for COVID-19 pandemic: A bibliometric analysis

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