Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2023
- DOI:
- 10.48550/arXiv.2306.07987
- arXiv:
- arXiv:2306.07987
- Bibcode:
- 2023arXiv230607987J
- Keywords:
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- Physics - Physics and Society;
- Statistics - Methodology