Accurate estimation of influenza epidemics using Google search data via ARGO
Accurate estimation of influenza epidemics using Google search data via ARGO
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DOI：
10.1073/pnas.1515373112
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发表时间：
2015-11-24
影响因子：
11.1
通讯作者：
Kou, S. C.
中科院分区：
文献类型：
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作者：
Yang, Shihao;Santillana, Mauricio;Kou, S. C.
准确实时跟踪流感爆发有助于公共卫生官员做出及时且有意义的决定，从而挽救生命。我们提出了一种流感跟踪模型 ARGO（使用 GOogle 搜索数据的自动回归），该模型使用公开的在线搜索数据。除了拥有严格的统计基础外，ARGO 的性能还优于所有以前可用的基于 Google 搜索的跟踪模型，包括最新版本的 Google Flu Trends，尽管它仅使用低质量的搜索数据作为来自公开可用的 Google Trends 和 Google Correlate 网站的输入。 ARGO 不仅考虑了流感流行的季节性，还捕捉了人们在线搜索行为随时间的变化。 ARGO 还具有灵活、自我修正、稳健和可扩展的特点，使其成为一种潜在的强大工具，可用于在多个时间和空间分辨率下实时跟踪其他社会事件。
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.