A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data
This provides a fast and reliable way to estimate illness impact for public health planning, but it is incremental as it builds on existing web data approaches.
The study tackled the problem of estimating influenza-like illness prevalence by developing a language-agnostic method using Wikipedia page views and machine learning, achieving state-of-the-art results in four European countries.
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.