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

In this paper, we propose a generic approach to perform global sensitivityanalysis (GSA) for compartmental models based on continuous-time Markov chains(CTMC). This approach enables a complete GSA for epidemic models, in which notonly the effects of uncertain parameters such as epidemic parameters(transmission rate, mean sojourn duration in compartments) are quantified, butalso those of intrinsic randomness and interactions between the two. The mainstep in our approach is to build a deterministic representation of theunderlying continuous-time Markov chain by controlling the latent variablesmodeling intrinsic randomness. Then, model output can be written as adeterministic function of both uncertain parameters and controlled latentvariables, so that it becomes possible to compute standard variance-basedsensitivity indices, e.g. the so-called Sobol' indices. However, differentsimulation algorithms lead to different representations. We exhibit in thiswork three different representations for CTMC stochastic compartmental modelsand discuss the results obtained by implementing and comparing GSAs based oneach of these representations on a SARS-CoV-2 epidemic model.

Exploiting deterministic algorithms to perform global sensitivity  analysis for continuous-time Markov chain compartmental models with  application to epidemiology