ArticleJournal of medical Internet research2020
Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets.JMIR formative research · 2024Article
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Twitter conversations predict the daily confirmed COVID-19 cases.Applied soft computing · 2022Article
- Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022Review
- Do you see what I see? Images of the COVID-19 pandemic through the lens of Google.Information processing & management · 2021Article
- High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
- Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers.Journal of medical Internet research · 2021Article
- The Influence of Media Coverage and Governmental Policies on Google Queries Related to COVID-19 Cutaneous Symptoms: Infodemiology Study.JMIR public health and surveillance · 2021Article
- Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: - evidence from Baidu index.BMC infectious diseases · 2021Article
- From data to decisions: The vital role of digital epidemiology for strengthening public health.Digital healthReview
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTimely 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.
objectiveWe 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.
methodsWe 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.
resultsPredictions 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.
conclusionsIdentifiable 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.
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