Your browser doesn't support javascript.
Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns.
Cousins, Henry C; Cousins, Clara C; Harris, Alon; Pasquale, Louis R.
  • Cousins HC; Department of Genetics, Stanford School of Medicine, Stanford, CA, United States.
  • Cousins CC; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Harris A; Department of Data Sciences, Dana-Farber Cancer Institute, Harvard TH Chan School of Public Health, Boston, MA, United States.
  • Pasquale LR; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, United States.
J Med Internet Res ; 22(7): e19483, 2020 07 30.
Article in English | MEDLINE | ID: covidwho-658765
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Population Surveillance / Coronavirus Infections / Public Health Informatics / Search Engine Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2020 Document Type: Article Affiliation country: 19483

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Population Surveillance / Coronavirus Infections / Public Health Informatics / Search Engine Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2020 Document Type: Article Affiliation country: 19483