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

State-space models have been widely used to model the dynamics ofcommunicable diseases in populations of interest by fitting to time-seriesdata. Particle filters have enabled these models to incorporate stochasticityand so can better reflect the true nature of population behaviours. Relevantparameters such as the spread of the disease, $R_t$, and recovery rates can beinferred using Particle MCMC. The standard method uses a Metropolis-Hastingsrandom-walk proposal which can struggle to reach the stationary distribution ina reasonable time when there are multiple parameters.  In this paper we obtain full Bayesian parameter estimations using gradientinformation and the No U-Turn Sampler (NUTS) when proposing new parameters ofstochastic non-linear Susceptible-Exposed-Infected-Recovered (SEIR) and SIRmodels. Although NUTS makes more than one target evaluation per iteration, weshow that it can provide more accurate estimates in a shorter run time thanMetropolis-Hastings.

Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal