ArticleJMIR public health and surveillance2023
Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.
Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Public Search Behavior and Tuberculosis Cases in Indonesia 2019-2023: An Infodemiology Study Using Google Trends .F1000Research · 2026Article
- Seasonal pattern, global search trend, and public interest in arthritis from 2004 to 2022: an infodemiology study.Immunologic research · 2025Article
- Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence.Communications medicine · 2025Article
- Toxicity on Social Media During the 2022 Mpox Public Health Emergency: Quantitative Study of Topical and Network Dynamics.Journal of medical Internet research · 2024Article
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
- Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.JMIR public health and surveillance · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks.
objectiveThis study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk.
methodsWe collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated.
resultsOur research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores.
conclusionsThe innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.
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