论文标题
使用互联网搜索数据准确跟踪状态级流感流行病
Use Internet Search Data to Accurately Track State-Level Influenza Epidemics
论文作者
论文摘要
对于流行病的控制和预防，及时的潜在热点见解是无价的。替代传统流行病监视，通常会落后于几周的实时落后于实时的，这提供了当前流行趋势的重要信息。在这里，我们提出了一种方法论，即Argox（使用Google Data Cross空间增强回归），以准确对美国的状态流感流行病的实时跟踪。 Argox将来自国家，地区和州一级的互联网搜索数据与疾病控制和预防中心的传统流感监视数据相结合，并既说明了州级流感活动的空间相关结构以及人们的互联网搜索模式的发展。与最佳替代方案相比，Argox平均达到28％的误差降低，用于2014年至2020年的实时状态级流感估计。Argox稳健且可靠，可以潜在地应用于追踪县和城市水平的流感活动和其他感染性疾病。
For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people's Internet search pattern. ARGOX achieves on average 28\% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases.
