%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 22
%N 7
%P e19483
%T Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns
%A Cousins,Henry C
%A Cousins,Clara C
%A Harris,Alon
%A Pasquale,Louis R
%+ Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, One Gustave L Levy Place, Box 1183, New York, NY, 10029, United States, 1 212 241 6752, louis.pasquale@mssm.edu
%K epidemiology
%K infoveillance
%K COVID-19
%K internet activity
%K Google Trends
%K infectious disease
%K surveillance
%K public health
%D 2020
%7 30.7.2020
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 32692691
%R 10.2196/19483
%U http://www.jmir.org/2020/7/e19483/
%U https://doi.org/10.2196/19483
%U http://www.ncbi.nlm.nih.gov/pubmed/32692691