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/

Zendy Plus

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

Zendy Tools

Zendy Open

The paper presents an algorithm for syndromic surveillance of an epidemicoutbreak formulated in the context of stochastic nonlinear filtering. Thedynamics of the epidemic is modeled using a generalized compartmentalepidemiological model with inhomogeneous mixing. The syndromic (typicallynon-medical) observations of the number of infected people (e.g. visits topharmacies, sale of certain products, absenteeism from work/study etc.) areused for estimation. The state of the epidemic, including the number ofinfected people and the unknown parameters of the model, are estimated via aparticle filter. The numerical results indicate that the proposed framework canprovide useful early prediction of the epidemic peak if the uncertainty inprior knowledge of model parameters is not excessive.

Monitoring and prediction of an epidemic outbreak using syndromic observations