DOI: 10.5061/dryad.r06h2
title: Data from: Adaptive nowcasting of influenza outbreaks using Google searches

description: Seasonal influenza outbreaks and pandemics of new strains of the influenza
 virus affect humans around the globe. However, traditional systems for
 measuring the spread of flu infections deliver results with one or two
 weeks delay. Recent research suggests that data on queries made to the
 search engine Google can be used to address this problem, providing
 real-time estimates of levels of influenza-like illness in a population.
 Others have however argued that equally good estimates of current flu
 levels can be forecast using historic flu measurements. Here, we build
 dynamic ‘nowcasting’ models; in other words, forecasting models that
 estimate current levels of influenza, before the release of official data
 one week later. We find that when using Google Flu Trends data in
 combination with historic flu levels, the mean absolute error (MAE) of
 in-sample ‘nowcasts’ can be significantly reduced by 14.4%, compared with
 a baseline model that uses historic data on flu levels only. We further
 demonstrate that the MAE of out-of-sample nowcasts can also be
 significantly reduced by between 16.0% and 52.7%, depending on the length
 of the sliding training interval. We conclude that, using adaptive models,
 Google Flu Trends data can indeed be used to improve real-time influenza
 monitoring, even when official reports of flu infections are available
 with only one week's delay.
description: Unweighted Percentages
 of Weekly Outpatient Visits for ILI and Google Flu Trends
 dataWe retrieved the weekly
 unweighted percentages of patient visits due to influenza-like illness
 (ILI), reported through the U.S. Outpatient Influenza-like Illness
 Surveillance Network (ILINet), from http://www.cdc.gov/flu/weekly/ on 10th
 December 2013. Here, ILI is defined as fever with a temperature of 100°F
 or greater, accompanied by a cough or a sore throat. Note that the data
 recorded for a given week can be updated in subsequent weeks, if the CDC
 have reason to believe that an updated figure would be more accurate.
 Here, we focus our analysis on the latest data available on the date of
 retrieval. We obtained the weekly time series of query volume for searches
 relating to ILI symptoms from Google Flu Trends
 (http://www.google.org/flutrends) on 18th December 2013. This time series
 is restricted to searches made in the United States, and has been shown by
 Ginsberg et al. to be correlated with the percentage of physician visits
 in which a patient presents with influenza-like symptoms. The creators of
 Google Flu Trends state that their algorithm for identifying influenza
 related searches is constantly evaluated against figures reported by the
 CDC and is occasionally updated to reflect changes in human online search
 behaviour. Since publication of the work carried out by Ginsberg et al.,
 the algorithm underwent updates in 2009 and 2013. Data analysed here is
 therefore an amalgamation of two different Google Flu Trends algorithms,
 with the transition occurring in August 2013. In both the patient visit
 and search engine query time series, weeks start on Sundays and end on
 Saturdays.PreisMoat2014.csv