39 citations · 64 across the 13 of their papers we have counts for
14 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…
A computationally efficient framework for realistic epidemic modelling through Gaussian Markov random fields
Angelos Alexopoulos, Paul Birrell, Daniela De Angelis
We tackle limitations of ordinary differential equation-driven Susceptible-Infections-Removed (SIR) models and their extensions that have recently be employed for epidemic nowcasti…
Estimating the duration of RT-PCR positivity for SARS-CoV-2 from doubly interval censored data with undetected infections
Joshua Blake, Paul Birrell, A. Sarah Walker +5
Monitoring the incidence of new infections during a pandemic is critical for an effective public health response. General population prevalence surveys for SARS-CoV-2 can provide h…
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…
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis
Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as 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…