dc.contributor.author | Cousins, Henry C | |
dc.contributor.author | Cousins, Clara C | |
dc.contributor.author | Harris, Alon | |
dc.contributor.author | Pasquale, Louis R | |
dc.date.accessioned | 2021-01-04T21:39:15Z | |
dc.date.available | 2021-01-04T21:39:15Z | |
dc.date.issued | 2020-07 | |
dc.date.submitted | 2020-07 | |
dc.identifier.issn | 1438-8871 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/128949 | |
dc.description.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. | en_US |
dc.publisher | JMIR Publications Inc. | en_US |
dc.relation.isversionof | http://dx.doi.org/10.2196/19483 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Journal of Medical Internet Research (JMIR) | en_US |
dc.title | Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Cousins, Henry C. et al. "Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns." Journal of Medical Internet Research 22, 7 (July 2020): e19483. © 2020 The Authors | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | en_US |
dc.relation.journal | Journal of Medical Internet Research | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.date.submission | 2021-01-04T18:15:13Z | |
mit.journal.volume | 22 | en_US |
mit.journal.issue | 7 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Complete | |