Sequential Bayesian analysis of time-varying transmission in stratified epidemic renewal models
Abstract
Understanding how transmission varies across age groups is important for epidemic surveillance and targeted intervention, but estimating age-specific transmission in near real time is challenging when infections are only partially observed, observations are delayed, and transmission varies across age groups. We develop a Bayesian age-stratified renewal model that jointly estimates latent infections, time-varying age-specific susceptibility, and age-specific contributions to transmission within an empirically specified contact structure. To enable sequential inference under these processes, we use a block particle filtering approach embedded within a sequential Monte Carlo squared framework, with fixed-lag smoothing to account for temporal dependence and delayed observations. From the inferred transmission process, we derive a next-generation matrix, effective reproduction number, and reproduction contributions. Applied to age-stratified COVID-19 surveillance data from Ireland during the Omicron dominant period, the framework reveals heterogeneity in susceptibility and transmission, with older adults exhibiting higher susceptibility, while younger and working-age groups make larger contributions to transmission because of their higher contact rates. These results demonstrate how statistical inference can provide age-specific transmission estimates to support targeted epidemic monitoring and intervention.