![]() ![]() Our model not only achieves the goal of ( i) dynamically incorporating new information from CDC reports as it becomes available and ( ii) automatically selecting the most useful Google search queries for estimation as in ref. This theoretical framework contains, as a special case, the model developed in ref. In addition, we provide a theoretical framework that, for the first time to our knowledge, justifies the prevailing use of linear models in the digital disease detection literature by incorporating causality arguments through a hidden Markov model. The methodology presented here produces robust and highly accurate ILI activity level estimates by addressing the three aforementioned shortcomings of the multiple GFT engines. ![]() Interestingly, Google has never made their raw data public, thus making it impossible to reproduce the exact results of GFT. Among them, GFT has received the most attention and has inspired subsequent digital disease detection systems ( 3, 8, 29– 32). In recent years, methods that harness Internet-based information have also been proposed, such as Google ( 1), Yahoo ( 2), and Baidu ( 3) Internet searches, Twitter posts ( 4), Wikipedia article views ( 5), clinicians’ queries ( 6), and crowdsourced self-reporting mobile apps such as Influenzanet (Europe) ( 26), Flutracking (Australia) ( 27), and Flu Near You (United States) ( 28). To alleviate this information gap, multiple methods combining climate, demographic, and epidemiological data with mathematical models have been proposed for real-time estimation of flu activity ( 18, 21– 25). This time lag is far from optimal for decision-making purposes. Here we present such a framework that culminates in a method that outperforms all existing methodologies for tracking influenza activity using internet search data.ĬDC’s ILI reports have a delay of 1–3wk due to the time for processing and aggregating clinical information. Although multiple articles have identified methodological flaws in GFT’s original algorithm ( 14– 16) and have led to incremental improvements ( 14, 16) (see also /2014/10/google-flu-trends-gets-brand-new-engine.html), a statistical framework that is theoretically sound and capable of accurate estimation is still lacking. However, significant discrepancies between GFT’s flu estimates and those measured by the Centers for Disease Control (CDC) in subsequent years led to considerable doubt about the value of digital disease detection systems ( 13). In 2009, Google Flu Trends (GFT), a digital disease detection system that uses the volume of selected Google search terms to estimate current influenza-like illnesses (ILI) activity, was identified by many as a good example of how big data would transform traditional statistical predictive analysis ( 12). Numerous studies have suggested great potential of these big data sets to detect/manage epidemic outbreaks, predict changes in stock prices ( 9, 10) and housing prices ( 11), etc. Big data sets are constantly generated nowadays as the activities of millions of users are collected from Internet-based services.
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