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 individual 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 seroprevalence 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.

翻译：我们提出一种灵活的贝叶斯证据综合方法，基于每日死亡人数对COVID-19的年龄特异性传播动态进行建模。通过由独立扩散过程驱动的适当降维公式，重建包含多种个体类型的人群中传播率的时间演化。采用量身定制的房室模型来学习潜在的感染计数，同时考虑由公共卫生干预和人类行为变化引起的传播波动。该模型基于英国、希腊和奥地利的公开COVID-19数据源进行拟合，并通过英格兰的大规模血清阳性率调查进行验证。特别地，我们展示了模型扩展如何在潜在层面促进证据调和。实现本研究的代码已通过Bernadette R包免费公开。