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Articles linked to this work by shared authors, journal, and citation graph.

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Authors' reply to the discussion of 'A COVID-19 Model for Local Authorities of the United Kingdom' by Mishra et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11 June 2021.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2024
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A COVID-19 model for local authorities of the United Kingdom.

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Using a latent Hawkes process for epidemiological modelling.

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Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England.

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Stratified epidemic model using a latent marked Hawkes process.

Stamatina Lamprinakou1, Axel Gandy2

  • 1Department of Statistics, Texas A&M University, College Station, USA.

Mathematical Biosciences
|July 20, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances epidemic modeling by incorporating age stratification, using a latent marked Hawkes process and Kernel Density Particle Filter (KDPF) for accurate infection tracking and forecasting. The age-structured model provides real-time insights into interventions and behavioral changes per group without significant computational increase.

Keywords:
Bayesian methodsCOVID-19EpidemicParticle filtersSelf-exciting processeslatent processes

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Computational Statistics

Background:

  • Existing epidemic models often assume homogeneous mixing, neglecting population structure.
  • Accurate real-time monitoring of disease spread and the impact of interventions is crucial.
  • Stratifying models by age can provide more granular insights into epidemic dynamics.

Purpose of the Study:

  • To extend an existing epidemic model to incorporate age stratification.
  • To model unobserved infections using a latent marked Hawkes process.
  • To infer epidemic dynamics and forecast trajectories using a Kernel Density Particle Filter (KDPF).

Main Methods:

  • Extension of the Lamprinakou et al. (2023) unstructured epidemic model.
  • Modeling unobserved infections with a latent marked Hawkes process.
  • Application of Kernel Density Particle Filter (KDPF) for inference and forecasting.

Main Results:

  • The age-stratified model accurately infers the marked counting process and instantaneous reproduction numbers per age group.
  • Incorporating age heterogeneity does not significantly increase computational cost compared to unstructured models.
  • The model effectively forecasts epidemic trajectories and provides real-time measurements of interventions' impact.

Conclusions:

  • Age stratification enhances epidemic modeling by providing group-specific insights.
  • The proposed KDPF-based inference method is computationally efficient and effective for age-structured epidemic data.
  • This methodology can be extended to other stratification factors beyond age.