Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, 02138, MA, USA.
Sci Rep. 2019 Mar 27;9(1):5238. doi: 10.1038/s41598-019-41559-6.
Accurate, high-resolution tracking of influenza epidemics at the regional level helps public health agencies make informed and proactive decisions, especially in the face of outbreaks. Internet users' online searches offer great potential for the regional tracking of influenza. However, due to the complex data structure and reduced quality of Internet data at the regional level, few established methods provide satisfactory performance. In this article, we propose a novel method named ARGO2 (2-step Augmented Regression with GOogle data) that efficiently combines publicly available Google search data at different resolutions (national and regional) with traditional influenza surveillance data from the Centers for Disease Control and Prevention (CDC) for accurate, real-time regional tracking of influenza. ARGO2 gives very competitive performance across all US regions compared with available Internet-data-based regional influenza tracking methods, and it has achieved 30% error reduction over the best alternative method that we numerically tested for the period of March 2009 to March 2018. ARGO2 is reliable and robust, with the flexibility to incorporate additional information from other sources and resolutions, making it a powerful tool for regional influenza tracking, and potentially for tracking other social, economic, or public health events at the regional or local level.
准确、高分辨率的区域流感跟踪有助于公共卫生机构做出明智和积极主动的决策，尤其是在面对疫情爆发时。互联网用户的在线搜索为区域流感跟踪提供了巨大的潜力。然而，由于数据结构复杂以及区域层面互联网数据质量下降，很少有既定方法能够提供令人满意的性能。在本文中，我们提出了一种名为 ARGO2（两步增强回归与谷歌数据）的新方法，该方法有效地结合了不同分辨率（国家和区域）的公开可用谷歌搜索数据以及疾病控制与预防中心（CDC）的传统流感监测数据，以实现准确、实时的区域流感跟踪。与现有的基于互联网数据的区域流感跟踪方法相比，ARGO2 在所有美国地区的表现都非常有竞争力，并且在我们针对 2009 年 3 月至 2018 年期间进行的数值测试中，其误差减少了 30%。ARGO2 可靠且稳健，具有从其他来源和分辨率纳入额外信息的灵活性，使其成为区域流感跟踪的有力工具，并且可能成为跟踪其他社会、经济或公共卫生事件的区域性或地方性工具。
