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Using a latent Hawkes process for epidemiological modelling

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  • Stamatina Lamprinakou
  • Axel Gandy
  • Emma McCoy

Abstract

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.

Suggested Citation

  • Stamatina Lamprinakou & Axel Gandy & Emma McCoy, 2023. "Using a latent Hawkes process for epidemiological modelling," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-27, March.
    • Handle: RePEc:plo:pone00:0281370
    DOI: 10.1371/journal.pone.0281370
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    References listed on IDEAS

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    1. Arianna Agosto & Paolo Giudici, 2020. "A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics," Risks, MDPI, vol. 8(3), pages 1-8, July.
    2. Shinsuke Koyama & Taiki Horie & Shigeru Shinomoto, 2021. "Estimating the time-varying reproduction number of COVID-19 with a state-space method," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-18, January.
    3. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
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