Using a latent Hawkes process for epidemiological modelling

• Published in: PloS one Vol. 18; no. 3; p. e0281370
• Main Authors: Lamprinakou, Stamatina, Gandy, Axel, McCoy, Emma
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.03.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Asymptomatic; Benchmarking; Biology and Life Sciences; Computer applications; Control; Coronaviruses; COVID-19; Disease transmission; Distribution (Probability theory); Epidemic models; Epidemics; Epidemiology; Estimates; Evaluation; Group Processes; Health aspects; Humans; Infection; Infections; Medicine and Health Sciences; Modelling; Ordinary differential equations; Physical Sciences; Population; Probability distribution; Quarantine; Research and Analysis Methods; United Kingdom; United Kingdom
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281370

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.