论文标题
扩散驱动的多类流行模型的贝叶斯分析，并应用于COVID-19
Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19
论文作者
论文摘要
我们考虑了一种灵活的贝叶斯证据综合方法，以基于每日死亡率计数对COVID-19的年龄特异性传播动态进行建模。包含多种类型个体的人群中传输速率的时间演变是通过由独立扩散过程驱动的适当减少尺寸的公式来重建的。适当量身定制的隔室模型用于学习感染的潜在人数，这是受公共卫生干预影响和人类行为变化影响的传播的波动。该模型适合来自英国，希腊和奥地利的免费可用的19个数据源，并使用英格兰的大规模血清阳性调查进行了验证。特别是，我们证明了模型扩展如何促进潜在层面的证据和解。实施此工作的代码可以通过Bernadette R软件包免费提供。
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.
