Quantifying efficiency gains of innovative designs of two-arm vaccine trials for COVID-19 using an epidemic simulation model

no code implementations • 13 Mar 2021 • Rob Johnson, Chris Jackson, Anne Presanis, Sofia S. Villar, Daniela De Angelis

Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial.

Trends in risks of severe events and lengths of stay for COVID-19 hospitalisations in England over the pre-vaccination era: results from the Public Health England SARI-Watch surveillance scheme

no code implementations • 8 Mar 2021 • Peter D. Kirwan, Suzanne Elgohari, Christopher H. Jackson, Brian D. M. Tom, Sema Mandal, Daniela De Angelis, Anne M. Presanis

Background: Trends in hospitalised case-fatality risk (HFR), risk of intensive care unit (ICU) admission and lengths of stay for patients hospitalised for COVID-19 in England over the pre-vaccination era are unknown.

Applications

MultiBUGS: Massively parallel MCMC for Bayesian hierarchical models

2 code implementations • 11 Apr 2017 • Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis, Andrew Thomas

MultiBUGS (https://multibugs. github. io) is a new version of the general-purpose Bayesian modelling software BUGS that implements a simple, generic algorithm for parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian hierarchical models.

Computation

Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis

1 code implementation • 27 Mar 2017 • Christopher Jackson, Anne Presanis, Stefano Conti, Daniela De Angelis

We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty.

Applications