A general method for estimating the prevalence of influenza-like-symptoms with Wikipedia data

• Published in: PloS one 2021-08, Vol.16 (8), p.e0256858
• Main Authors: De Toni, Giovanni, Consonni, Cristian, Montresor, Alberto
• Format: Article
• Language: English
• Subjects: Algorithms; Belgium - epidemiology; Big Data; Biology and Life Sciences; Computer science; Datasets; Development and progression; Disease; Distribution; Encyclopedias; Encyclopedias as Topic; Estimation; Feasibility studies; Generalized linear models; Germany - epidemiology; Humans; Illnesses; Influenza; Influenza, Human - epidemiology; Information Seeking Behavior; Internet; Italy - epidemiology; Learning algorithms; Machine learning; Medical literature; Medicine and Health Sciences; Netherlands - epidemiology; People and Places; Prevalence; Search algorithms; Signs and symptoms; Social Sciences; Technology application
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0256858

Influenza is an acute respiratory seasonal disease that affects millions of people worldwide and causes thousands of deaths in Europe alone. Estimating 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 machine learning models and information about Wikipedia's page views of a selected group of articles 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 can reach state-of-the-art results by comparing it with previous solutions.