Can a latent Hawkes process be used for epidemiological modelling?
This provides a novel epidemiological model for researchers and public health officials to better understand and predict COVID-19 dynamics, though it appears incremental as it builds on existing Hawkes process methods.
The authors tackled the problem of modeling COVID-19 spread by introducing a latent Hawkes process with temporal covariates to estimate infections and reproduction numbers, demonstrating performance on synthetic data and UK COVID-19 cases with benchmarking against alternative approaches.
Understanding the spread of COVID-19 has been the subject of numerous studies, highlighting the significance of reliable epidemic models. Here, we introduce a novel epidemic model using a latent Hawkes process with temporal covariates for modelling the infections. Unlike other models, we model the reported cases via a probability distribution driven by the underlying Hawkes process. Modelling the infections via a Hawkes process allows us to estimate by whom an infected individual was infected. We propose a Kernel Density Particle Filter (KDPF) for inference of both latent cases and reproduction number and for predicting the new cases in the near future. The computational effort is proportional to the number of infections making it possible to use particle filter type algorithms, such as the KDPF. We demonstrate the performance of the proposed algorithm on synthetic data sets and COVID-19 reported cases in various local authorities in the UK, and benchmark our model to alternative approaches.