COVID-19: Bayesian inference for high resolution stochastic modelling for the UK

We will develop an efficient and robust MCMC-based framework for fully Bayesian inference methodology for spatially explicit, stochastic, and partially observed meta-population epidemic models within human populations. This will be applied to the current UK Covid-19 epidemic. We particularly focus on the challenge of providing continuously updated parameter estimation and risk assessment in the face of censored data observations and hence detailed age- and space-specific predictions of Covid-19 prevalence and incidence in the UK. Our predictions will be targeted at disease management, providing early warning of spatial "hotspots" of epidemic resurgence as Behavioural and Social Intervention (lockdown) measures are lifted, as well as informing targeted disease surveillance to space- and age-related sub-populations. We respond to the observation that existing differential equation based models informing SAGE cannot operate at high population resolution, since as meta-populations get smaller, stochastic fluctuations intrinsic to the epidemic process begin to dominate case observation noise. Whilst stochastic models of Covid-19 spread (based on pre-existing influenza models) do exist, methods to fit them at scale in the face of changing data availability require development. To address this, we will extend our existing Bayesian approach to real-time risk prediction for individual level models, developing a data-augmentation MCMC approach to state-transition models defined on high-dimensional meta-population structures. Inference and forward simulation algorithms will be built using Google's TensorFlow library, providing appropriate balance between rapid algorithm development and fast GPU-accelerated computations to ensure that our results are timely, and appropriate for Covid-19 decision support across the UK.