tetano
Editor, Senior Moderator
Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season
Methods
In 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.
Results
The 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.
Discussion
This study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.
full article
https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.21.1900221#html_fulltext
- Paul P Schneider[SUP]1,2[/SUP], Christel JAW van Gool[SUP]3[/SUP], Peter Spreeuwenberg[SUP]2[/SUP], Mari?tte Hooiveld[SUP]2[/SUP], G? A Donker[SUP]2[/SUP], David J Barnett[SUP]4[/SUP], John Paget[SUP]2[/SUP]
Methods
In 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.
Results
The 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.
Discussion
This study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.
full article
https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.21.1900221#html_fulltext