Bouranis, Lampros, Demiris, Nikolaos, Kalogeropoulos, Konstantinos 
ORCID: 0000-0002-0330-9105 and Ntzoufras, Ioannis
(2025)
Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19.
Journal of the Royal Statistical Society. Series A: Statistics in Society.
ISSN 0964-1998
Abstract
We consider a flexible Bayesian evidence synthesis approach to model the age-specific transmission dynamics of COVID-19 based on daily mortality counts. The temporal evolution of transmission rates in populations containing multiple types of individuals is reconstructed via an appropriate dimension-reduction formulation driven by independent diffusion processes. A suitably tailored compartmental model is used to learn the latent counts of infection, accounting for fluctuations in transmission influenced by public health interventions and changes in human behaviour. The model is fitted to freely available COVID-19 data sources from the UK, Greece, and Austria and validated using a large-scale prevalence survey in England. In particular, we demonstrate how model expansion can facilitate evidence reconciliation at a latent level. The code implementing this work is made freely available via the Bernadette R package.
| Item Type: | Article |
|---|---|
| Additional Information: | © 2025 The Royal Statistical Society |
| Divisions: | Statistics |
| Subjects: | Q Science > QA Mathematics R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine H Social Sciences > HV Social pathology. Social and public welfare. Criminology |
| Date Deposited: | 02 Sep 2025 08:00 |
| Last Modified: | 02 Sep 2025 08:00 |
| URI: | http://eprints.lse.ac.uk/id/eprint/129347 |
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