English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Influenza is an acute respiratory seasonal disease that affects millions ofpeople worldwide and causes thousands of deaths in Europe alone. Being able toestimate in a fast and reliable way the impact of an illness on a given countryis essential to plan and organize effective countermeasures, which is nowpossible by leveraging unconventional data sources like web searches andvisits. In this study, we show the feasibility of exploiting information aboutWikipedia's page views of a selected group of articles and machine learningmodels to obtain accurate estimates of influenza-like illnesses incidence infour European countries: Italy, Germany, Belgium, and the Netherlands. Wepropose a novel language-agnostic method, based on two algorithms, PersonalizedPageRank and CycleRank, to automatically select the most relevant Wikipediapages to be monitored without the need for expert supervision. We then show howour model is able to reach state-of-the-art results by comparing it withprevious solutions.

A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data