Uncovering the Time-Frequency Relationship Between Google Trends and COVID-19 Vaccination Metrics: A Hybrid ARDL-Wavelet Coherence Model for Prediction

The results show that Google Trends has good long-term prediction ability in the vaccine promotion stage, but its short-term prediction effect shows volatility with time and frequency. The ARDL-wavelet coherence analysis framework proposed in this paper provides a new research paradigm for evaluating the public's influence on vaccination. The research results emphasize the potential and importance of Google Trends in the construction of real-time public health response and early warning systems.

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Publication Details

Journal
Cureus
Published
2025-09-25
DOI
https://doi.org/10.7759/cureus.93193
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.00
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article

Uncovering the Time-Frequency Relationship Between Google Trends and COVID-19 Vaccination Metrics: A Hybrid ARDL-Wavelet Coherence Model for Prediction

Zongjing Liang, Yun Kuang, Liang Ge, Zhijie Li
Cureus
Data-Driven Disease Surveillance
article

Uncovering the Time-Frequency Relationship Between Google Trends and COVID-19 Vaccination Metrics: A Hybrid ARDL-Wavelet Coherence Model for Prediction

Zongjing Liang, Yun Kuang, Liang Ge, Zhijie Li
article en

Abstract

The results show that Google Trends has good long-term prediction ability in the vaccine promotion stage, but its short-term prediction effect shows volatility with time and frequency. The ARDL-wavelet coherence analysis framework proposed in this paper provides a new research paradigm for evaluating the public's influence on vaccination. The research results emphasize the potential and importance of Google Trends in the construction of real-time public health response and early warning systems.

CureusVol. 17(9)
Guangxi Normal University (CN)
Openalex Percentile: Top 65%
Data-Driven Disease Surveillance
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