Dataset Information

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

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.

SUBMITTER: Cousins HC

PROVIDER: S-EPMC7394521 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Cousins Henry C HC Cousins Clara C CC Harris Alon A Pasquale Louis R LR

Journal of medical Internet research 20200730 7

<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>We used regional confirmed case data from the New York Times and Goog ...[more]

PMID: 32692691

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Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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