Management School, University of Chinese Academy of Sciences, Beijing, China.
PLoS One. 2013 May 30;8(5):e64323. doi: 10.1371/journal.pone.0064323. Print 2013.
Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China.
已经提出了几种用于实时检测和预测流感传播的方法。这些方法包括搜索与流感相关的术语查询数据，该方法已被探索作为增强传统监测方法的工具。在本文中，我们提出了一种使用百度的互联网搜索查询数据来模拟和监测中国流感活动的方法。本研究的目的是提出一种全面的技术，用于：（i）关键词选择，（ii）关键词过滤，（iii）指数构成和（iv）中国流感活动的建模和检测。选定复合关键字索引的顺序时间序列与中国流感病例数据具有显著相关性。此外，2012 年前 8 个月的流感病例提前一个月预测的平均绝对百分比误差小于 11%。据我们所知，这是首次使用百度的搜索查询数据结合这种方法来估计中国的流感活动。
