Age-stratified epidemic model using a latent marked Hawkes process

TL;DR

This paper introduces an age-stratified epidemic model using a latent marked Hawkes process, enabling real-time inference and forecasting of COVID-19 spread across different age groups with improved accuracy.

Contribution

It extends existing epidemic models by incorporating age stratification and latent Hawkes processes, enhancing real-time inference and forecasting capabilities.

Findings

Age stratification reduces uncertainty in estimates.

The model accurately forecasts epidemic trajectories.

Incorporating heterogeneity improves intervention assessment.

Abstract

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.