Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak
Physics and Society
2023-07-18 v3 Methodology
Abstract
Stochastic epidemic models which incorporate interactions between space and human mobility are a key tool to inform prioritisation of outbreak control to appropriate locations. However, methods for fitting such models to national-level population data are currently unfit for purpose due to the difficulty of marginalising over high-dimensional, highly-correlated censored epidemiological event data. Here we propose a new Bayesian MCMC approach to inference on a spatially-explicit stochastic SEIR meta-population model, using a suite of novel model-informed Metropolis-Hastings samplers. We apply this method to UK COVID-19 case data, showing real-time spatial results that were used to inform UK policy during the pandemic.
Keywords
Cite
@article{arxiv.2306.07987,
title = {Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak},
author = {Chris P Jewell and Alison C Hale and Barry S Rowlingson and Christopher Suter and Jonathan M Read and Gareth O Roberts},
journal= {arXiv preprint arXiv:2306.07987},
year = {2023}
}