Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 112, Issue 47, Pages 14473-14478Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1515373112
Keywords
digital disease detection; seasonal influenza; big data; influenza-like illnesses activity real-time estimation; autoregressive exogenous model
Categories
Funding
- National Science Foundation [DMS-1510446]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1510446] Funding Source: National Science Foundation
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Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.
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