Abstract
BackgroundDespite the early development of Google Flu Trends in 2009, standards for digital epidemiology methods have not been established and research from European countries is scarce.AimIn this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time.MethodsIn this retrospective analysis, we simulated the weekly use of a prediction model for estimating the then-current ILI incidence across the 2017/18 influenza season solely based on Google search query data. We used weekly ILI data as reported to The European Surveillance System (TESSY) each week, and we removed the then-last 4 weeks from our dataset. We then fitted a prediction model based on the then-most-recent search query data from Google Trends to fill the 4-week gap (‘Nowcasting’). Lasso regression, in combination with cross-validation, was applied to select predictors and to fit the 52 models, one for each week of the season.ResultsThe models provided accurate predictions with a mean and maximum absolute error of 1.40 (95% confidence interval: 1.09–1.75) and 6.36 per 10,000 population. The onset, peak and end of the epidemic were predicted with an error of 1, 3 and 2 weeks, respectively. The number of search terms retained as predictors ranged from three to five, with one keyword, ‘griep’ (‘flu’), having the most weight in all models.DiscussionThis study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.
Highlights
Previous studies suggest that traditional disease surveillance systems could be complemented with information from online data sources [1,2,3]
Aim: In this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time
This study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data
Summary
Previous studies suggest that traditional disease surveillance systems could be complemented with information from online data sources [1,2,3]. With influenza-like illness (ILI), individuals might search for information about symptoms, look for remedies or share messages on social media All of these interactions leave digital footprints, which, when aggregated, could be harnessed to monitor disease activity [1]. Online data streams could be used to support the timely detection of infectious disease outbreaks This hypothesis is not new, and in 2009, researchers at Google reported that their Flu Trends model was able to predict ILI activity in the United States (US) in real time, by monitoring millions of queries on their search engine [5]. Methods: In this retrospective analysis, we simulated the weekly use of a prediction model for estimating the -current ILI incidence across the 2017/18 influenza season solely based on Google search query data. Discussion: This study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data
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