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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The importance of spatial networks in the spread of an epidemic is anessential aspect in modeling the dynamics of an infectious disease.Additionally, any realistic data-driven model must take into account the largeuncertainty in the values reported by official sources, such as the amount ofinfectious individuals. In this paper we address the above aspects through ahyperbolic compartmental model on networks, in which nodes identify locationsof interest, such as cities or regions, and arcs represent the ensemble of mainmobility paths. The model describes the spatial movement and interactions of apopulation partitioned, from an epidemiological point of view, on the basis ofan extended compartmental structure and divided into commuters, moving on asuburban scale, and non-commuters, acting on an urban scale. Through adiffusive rescaling, the model allows us to recover classical diffusionequations related to commuting dynamics. The numerical solution of theresulting multiscale hyperbolic system with uncertainty is then tackled using astochastic collocation approach in combination with a finite-volume IMEXmethod. The ability of the model to correctly describe the spatialheterogeneity underlying the spread of an epidemic in a realistic city networkis confirmed with a study of the outbreak of COVID-19 in Italy and its spreadin the Lombardy Region.

Hyperbolic compartmental models for epidemic spread on networks with uncertain data: application to the emergence of Covid-19 in Italy