Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Sep 5, 2022
Date Accepted: Jun 29, 2023
Enhancing the predictive power of Google Trends data through network analysis: An infodemiology study of COVID-19
ABSTRACT
Background:
The 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 measure for various official statistics and public health issues. Previous studies have tended to use relative search volumes (RSVs) 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 weaken the data resolution in reflecting the actual searching behaviors, and such weakening can limit the potential for using GT data to predict disease outbreaks.
Objective:
This study introduced a merged algorithm that helps recover the resolution and accuracy of the search volume data extract from GT over long observation periods. In addition, we extended the application of merged search volumes (MSVs) and used network analysis in tracking COVID-19 pandemic risk.
Methods:
We collected RSVs from GT and transform them into MSVs using our proposed merged algorithm. The MSVs of selected coronavirus-related keywords were compiled through the rolling-window method. The network statistics, including network density and the global clustering coefficients between the MSVs, were calculated to form dynamic networks.
Results:
Our research findings suggested that even GT restricts the search data retrieval into weekly data points over a long time 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.
Conclusions:
The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful to predict the pandemic risk. Further investigation of the GT dynamic network can focus on non-communicable diseases, health-related behaviors, and misinformation on the Internet.
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