  Influenza is an acute respiratory seasonal disease that affects millions of
people worldwide and causes thousands of deaths in Europe alone. Being able to
estimate in a fast and reliable way the impact of an illness on a given country
is essential to plan and organize effective countermeasures, which is now
possible by leveraging unconventional data sources like web searches and
visits. In this study, we show the feasibility of exploiting information about
Wikipedia's page views of a selected group of articles and machine learning
models to obtain accurate estimates of influenza-like illnesses incidence in
four European countries: Italy, Germany, Belgium, and the Netherlands. We
propose a novel language-agnostic method, based on two algorithms, Personalized
PageRank and CycleRank, to automatically select the most relevant Wikipedia
pages to be monitored without the need for expert supervision. We then show how
our model is able to reach state-of-the-art results by comparing it with
previous solutions.

Computers and Society;Machine Learning;Social and Information Networks