62 citations · 313 across the 18 of their papers we have counts for
18 papers
GENIE: Generative Neural Inference for Epidemics
Laura M. Guzmán-Rincón, George R. E. Bradley, Joel Kandiah +5
The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epid…
Real-time modelling of the SARS-CoV-2 pandemic in England 2020-2023: a challenging data integration
Paul J Birrell, Joshua Blake, Joel Kandiah +12
A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission…
The NOSTRA model: coherent estimation of infection sources in the case of possible nosocomial transmission
David J Pascall, Chris Jackson, Stephanie Evans +8
Nosocomial infections have important consequences for patients and hospital staff: they worsen patient outcomes and their management stresses already overburdened health systems. A…
The Lifebelt Particle Filter for robust estimation from low-valued count data
Alice Corbella, Trevelyan J. McKinley, Paul J. Birrell +4
Particle filtering methods can be applied to estimation problems in discrete spaces on bounded domains, to sample from and marginalise over unknown hidden states. As in continuous…
An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling
Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis
Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of tran…
Inferring Epidemics from Multiple Dependent Data via Pseudo-Marginal Methods
Alice Corbella, Anne M Presanis, Paul J Birrell +1
Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from th…
A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19
Christopher Jackson, Brian Tom, Peter Kirwan +5
We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are a…
Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study
Peter Kirwan, Andre Charlett, Paul Birrell +5
Widespread vaccination campaigns have changed the landscape for COVID-19, vastly altering symptoms and reducing morbidity and mortality. We estimate trends in mortality by month of…
Quantifying efficiency gains of innovative designs of two-arm vaccine trials for COVID-19 using an epidemic simulation model
Rob Johnson, Chris Jackson, Anne Presanis +2
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 th…
MultiBUGS: A parallel implementation of the BUGS modelling framework for faster Bayesian inference
Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis +1
MultiBUGS (https://www.multibugs.org) is a new version of the general-purpose Bayesian modelling software BUGS that implements a generic algorithm for parallelising Markov chain Mo…
Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis
Christopher Jackson, Anne Presanis, Stefano Conti +1
Suppose we have a Bayesian model which combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model,…
Efficient real-time monitoring of an emerging influenza epidemic: how feasible?
Paul J Birrell, Lorenz Wernisch, Brian D M Tom +4
A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK rev…
Joining and splitting models with Markov melding
Robert J. B. Goudie, Anne M. Presanis, David Lunn +2
Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we intro…
Synthesising evidence to estimate pandemic (2009) A/H1N1 influenza severity in 2009-2011
Anne M. Presanis, Richard G. Pebody, Paul J. Birrell +5
Knowledge of the severity of an influenza outbreak is crucial for informing and monitoring appropriate public health responses, both during and after an epidemic. However, case-fat…
Estimation of HIV Burden through Bayesian Evidence Synthesis
Daniela De Angelis, Anne M. Presanis, Stefano Conti +1
Planning, implementation and evaluation of public health policies to control the human immunodeficiency virus (HIV) epidemic require regular monitoring of disease burden. This incl…
Reconstructing transmission trees for communicable diseases using densely sampled genetic data
Colin J. Worby, Philip D. O'Neill, Theodore Kypraios +5
Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding imp…
Conflict Diagnostics in Directed Acyclic Graphs, with Applications in Bayesian Evidence Synthesis
Anne M. Presanis, David Ohlssen, David J. Spiegelhalter +1
Complex stochastic models represented by directed acyclic graphs (DAGs) are increasingly employed to synthesise multiple, imperfect and disparate sources of evidence, to estimate q…
Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach
Stefano Conti, Anne M. Presanis, Maaike G. van Veen +4
Multi-parameter evidence synthesis (MPES) is receiving growing attention from the epidemiological community as a coherent and flexible analytical framework to accommodate a dispara…