Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns
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...
| Main Authors: | , , , |
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| Format: | Article |
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JMIR Publications Inc.
2021
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| Online Access: | https://hdl.handle.net/1721.1/128949 |
| _version_ | 1811077683443924992 |
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| author | Cousins, Henry C Cousins, Clara C Harris, Alon Pasquale, Louis R |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Cousins, Henry C Cousins, Clara C Harris, Alon Pasquale, Louis R |
| author_sort | Cousins, Henry C |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:46:54Z |
| format | Article |
| id | mit-1721.1/128949 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:46:54Z |
| publishDate | 2021 |
| publisher | JMIR Publications Inc. |
| record_format | dspace |
| spelling | mit-1721.1/1289492022-09-30T22:59:29Z Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns Cousins, Henry C Cousins, Clara C Harris, Alon Pasquale, Louis R Massachusetts Institute of Technology. Department of Biological Engineering 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. 2021-01-04T21:39:15Z 2021-01-04T21:39:15Z 2020-07 2020-07 Article http://purl.org/eprint/type/JournalArticle 1438-8871 https://hdl.handle.net/1721.1/128949 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 http://dx.doi.org/10.2196/19483 Journal of Medical Internet Research Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf JMIR Publications Inc. Journal of Medical Internet Research (JMIR) |
| spellingShingle | Cousins, Henry C Cousins, Clara C Harris, Alon Pasquale, Louis R Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns |
| title | Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns |
| title_full | Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns |
| title_fullStr | Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns |
| title_full_unstemmed | Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns |
| title_short | Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns |
| title_sort | regional infoveillance of covid 19 case rates analysis of search engine query patterns |
| url | https://hdl.handle.net/1721.1/128949 |
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