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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014-07-02
摘要
Background: Google Flu Trends (GFT) claimed to generate real-time, valid predictions of 族群 influenza-like illness (ILI) using search queries, heralding acclaim and replication across public health. However, recent studies have questioned the validity of GFT. Purpose: To propose an alternative methodology that better realizes the potential of GFT, with collateral value for digital 疾病 detection broadly. Methods: Our alternative method automatically selects specific queries to monitor and autonomously updates the model each week as new 資訊 about CDC-reported ILI becomes available, as developed in 2013. Root mean squared errors (RMSEs) and Pearson correlations comparing predicted ILI (proportion of patient visits indicative of ILI) with subsequently observed ILI were used to judge model perform
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