Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns
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Author(s)
Cousins, Henry C
Cousins, Clara C
Harris, Alon
Pasquale, Louis R
Date Issued
July 2020
Journal
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Citation
Cousins, Henry C. et al. "Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns." Journal of Medical Internet Research 22, 7 (July 2020): e19483. © 2020 The Authors
Version
Final published version
Abstract
Background: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed.
Objective: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States.
Methods: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels.
Results: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05.
Conclusions: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.2196/19483
