TY  - JOUR
AU  - Cousins, Henry C
AU  - Cousins, Clara C
AU  - Harris, Alon
AU  - Pasquale, Louis R
PY  - 2020
DA  - 2020/7/30
TI  - Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns
JO  - J Med Internet Res
SP  - e19483
VL  - 22
IS  - 7
KW  - epidemiology
KW  - infoveillance
KW  - COVID-19
KW  - internet activity
KW  - Google Trends
KW  - infectious disease
KW  - surveillance
KW  - public health
AB  - 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. 
SN  - 1438-8871
UR  - http://www.jmir.org/2020/7/e19483/
UR  - https://doi.org/10.2196/19483
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32692691
DO  - 10.2196/19483
ID  - info:doi/10.2196/19483
ER  -