Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns

Journal of Medical Internet Research
Henry C CousinsLouis R Pasquale

Abstract

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. 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. 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. 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 r...Continue Reading

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Citations

Feb 17, 2021·Aesthetic Surgery Journal·Christopher P BellairePeter J Taub
Jul 18, 2021·International Journal of Infectious Diseases : IJID : Official Publication of the International Society for Infectious Diseases·Atina HusnayainEmily Chia-Yu Su

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Google Trends ( GT )
GT
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