{
  "abstract": "Mass gathering events (MGEs) can create intense, short-lived transmission hotspots whose effects propagate through urban populations. Quantifying these effects requires linking fine-scale interactions during gatherings with epidemic transmission across metropolitan mobility networks. We developed a hybrid multiscale simulation framework coupling empirically derived individual-based contact networks for MGEs with a stochastic metapopulation susceptible-exposed-infected-recovered (SEIR) model. Using population and commuting data from Madrid, Spain, we investigated how event-related transmission affects epidemic dynamics and district-level invasion. Across the scenarios examined, MGEs increased epidemic peak incidence and accelerated epidemic progression, although the magnitude of these effects depended on event-specific contact patterns and background transmissibility. Dense contact patterns produced greater epidemic amplification, whereas measures reducing close-contact interactions weakened these effects. MGEs also advanced epidemic invasion across districts and increased the temporal synchronization of arrival times, producing a more synchronized metropolitan epidemic wave. The association between MGEs and earlier district-level invasion was partly explained by the size of the event-generated epidemic seed. The main patterns persisted across the epidemiological parameter ranges examined. The framework provides a multiscale approach for investigating how mass gathering management may influence metropolitan epidemic dynamics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.35162",
  "author_total": 4,
  "authors": [
    {
      "name": "Jaeyoung Kwak"
    },
    {
      "name": "Michael H. Lees"
    },
    {
      "name": "Chin Chun Ooi"
    },
    {
      "name": "Wentong Cai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bd99vm219zrbv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.35162",
  "fulltext_url": null,
  "id": "bd99vm219zrbv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.35162",
  "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",
      "cs.CY"
    ],
    [
      "arxiv",
      "cs.MA"
    ],
    [
      "arxiv",
      "physics.soc-ph"
    ],
    [
      "arxiv",
      "q-bio.PE"
    ],
    [
      "openalex",
      "2739|Mathematical and Theoretical Epidemiology and Ecology Models"
    ]
  ],
  "openalex": "W7214772517",
  "pdf_url": "https://arxiv.org/pdf/2609.35162",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bd99vm219zrbv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.35162",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.35162"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.PE",
      "subfield": "evolutionary-biology"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.soc-ph",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Hybrid epidemic simulation framework coupling equation-based and individual-based models",
  "title_text": "Hybrid epidemic simulation framework coupling equation-based and individual-based models",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:13Z",
  "url": "https://research.draconitic.com/paper/bd99vm219zrbv",
  "venue": "arXiv",
  "versions": []
}