Data from: Adaptive nowcasting of influenza outbreaks using Google searches

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.