  We extend the unstructured homogeneously mixing epidemic model introduced by
Lamprinakou et al. [arXiv:2208.07340] considering a finite population
stratified by age bands. We model the actual unobserved infections using a
latent marked Hawkes process and the reported aggregated infections as random
quantities driven by the underlying Hawkes process. We apply a Kernel Density
Particle Filter (KDPF) to infer the marked counting process, the instantaneous
reproduction number for each age group and forecast the epidemic's future
trajectory in the near future; considering the age bands and the population
size does not increase the computational effort. We demonstrate the performance
of the proposed inference algorithm on synthetic data sets and COVID-19
reported cases in various local authorities in the UK. We illustrate that
taking into account the individual heterogeneity in age decreases the
uncertainty of estimates and provides a real-time measurement of interventions
and behavioural changes.
