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Their search log contains the IP address of the user, which could be used to trace back to the region where the search query is originally submitted. Google Flu Trends tries to avoid privacy violations by only aggregating millions of anonymous search queries, without identifying individuals that performed the search. This algorithm has been subsequently revised by Google, partially in response to concerns about accuracy, and attempts to replicate its results have suggested that the algorithm developers "felt an unarticulated need to cloak the actual search terms identified". Finally, the trained model is used to predict flu outbreak across all regions in the United States. Using the sum of top 45 ILI-related queries, the linear model is fitted to the weekly ILI data between 20 so that the coefficient can be gained. Then the top 45 queries are chosen because, when aggregated together, these queries fit the history data the most accurately. This process produces a list of top queries which gives the most accurate predictions of CDC ILI data when using the linear model. Centers for Disease Control and Prevention (CDC). Įach of the 50 million queries is tested as Q to see if the result computed from a single query could match the actual history ILI data obtained from the U.S. β 0 is the intercept and β 1 is the coefficient, while ε is the error term. P is the percentage of ILI physician visit and Q is the ILI-related query fraction computed in previous steps. By identifying the IP address associated with each search, the state in which this query was entered can be determined.Ī linear model is used to compute the log-odds of Influenza-like illness (ILI) physician visit and the log-odds of ILI-related search query:
#Google trends series
A query's time series is computed separately for each state and normalized into a fraction by dividing the number of each query by the number of all queries in that state. įirst, a time series is computed for about 50 million common queries entered weekly within the United States from 2003 to 2008. Google Flu Trends was described as using the following method to gather information about flu trends. Roni Zeiger helped develop Google Flu Trends. These estimates have been generally consistent with conventional surveillance data collected by health agencies, both nationally and regionally. Google Flu Trends compared these findings to a historic baseline level of influenza activity for its corresponding region and then reports the activity level as either minimal, low, moderate, high, or intense. The idea behind Google Flu Trends was that, by monitoring millions of users’ health tracking behaviors online, the large number of Google search queries gathered can be analyzed to reveal if there is the presence of flu-like illness in a population.