{
  "abstract": "Understanding how transmission varies across age groups is important for epidemic surveillance and targeted intervention, but estimating age-specific transmission in near real time is challenging when infections are only partially observed, observations are delayed, and transmission varies across age groups. We develop a Bayesian age-stratified renewal model that jointly estimates latent infections, time-varying age-specific susceptibility, and age-specific contributions to transmission within an empirically specified contact structure. To enable sequential inference under these processes, we use a block particle filtering approach embedded within a sequential Monte Carlo squared framework, with fixed-lag smoothing to account for temporal dependence and delayed observations. From the inferred transmission process, we derive a next-generation matrix, effective reproduction number, and reproduction contributions. Applied to age-stratified COVID-19 surveillance data from Ireland during the Omicron dominant period, the framework reveals heterogeneity in susceptibility and transmission, with older adults exhibiting higher susceptibility, while younger and working-age groups make larger contributions to transmission because of their higher contact rates. These results demonstrate how statistical inference can provide age-specific transmission estimates to support targeted epidemic monitoring and intervention.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03663",
  "author_total": 2,
  "authors": [
    {
      "name": "Dhorasso Temfack"
    },
    {
      "name": "Jason Wyse"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dx43017ygg7w9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03663",
  "fulltext_url": null,
  "id": "dx43017ygg7w9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03663",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03663",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dx43017ygg7w9.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:34Z",
      "native_id": "2611.03663",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03663"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Sequential Bayesian analysis of time-varying transmission in stratified epidemic renewal models",
  "title_text": "Sequential Bayesian analysis of time-varying transmission in stratified epidemic renewal models",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:10Z",
  "url": "https://research.draconitic.com/paper/dx43017ygg7w9",
  "venue": "arXiv",
  "versions": []
}