{
  "abstract": "Forecasting emerging infectious disease outbreaks is particularly challenging for sexual networks of men who have sex with men (MSM), where transmission can be shaped by heterogeneous and overdispersed contact structures, mixing assortativity, and both one-time and recurrent partnerships. Using stochastic epidemic simulations on synthetic MSM networks and empirical mpox case data from major cities, we systematically evaluated five classes of short-term forecasting models, from random walk approaches to semi-mechanistic attenuated renewal processes. Model performance was assessed using the continuous ranked probability score (CRPS). Across both simulated and empirical epidemics in heterogeneous networks, renewal models attenuated by a reverse-sigmoid curve, to account for early depletion of highly-connected nodes, consistently outperformed alternatives, achieving more balanced and reliable forecasts. Our findings suggest a practical approach for robust forecasting in highly overdispersed networks, where distinguishing network effects from other factors remains challenging and usually requires additional data. Author summary: Forecasting infectious disease outbreaks is particularly difficult in networks of men who have sex with men (MSM). These networks are structurally complex: contact rates are highly skewed, partnerships are shaped by degree- and role-based assortativity, and transmission can occur through both one-time and recurrent partnerships. Together, these features produce epidemic dynamics that are hard to predict with standard models, which typically assume contacts are distributed more evenly across a population.",
  "abstract_shown": true,
  "affiliations": [
    "Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.",
    "Modelling Division, Analysis and Intelligence Assessment, Chief Data Officer Group, United Kingdom Health Security Agency, London, United Kingdom.",
    "Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "William D Green",
      "orcid": "0000-0001-8556-0389"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Christopher E Overton",
      "orcid": "0000-0002-8433-4010"
    },
    {
      "affs": [
        1
      ],
      "name": "Thomas Ward"
    },
    {
      "affs": [
        0
      ],
      "name": "Ciara V McCarthy"
    },
    {
      "affs": [
        0
      ],
      "name": "Sebastian Funk"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2jz55aq8cg454.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1013987",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013987",
  "id": "2jz55aq8cg454",
  "issns": [
    "1553-7358",
    "1553-734X"
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  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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    ],
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      "2739|Mathematical and Theoretical Epidemiology and Ecology Models"
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      "plos",
      "/Computer and information sciences/Network analysis"
    ],
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      "plos",
      "/Earth sciences/Geography/Human geography/Urban geography/Cities"
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      "plos",
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      "/Medicine and health sciences/Epidemiology/Infectious disease epidemiology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Infectious diseases/Infectious disease epidemiology"
    ],
    [
      "plos",
      "/People and places/Geographical locations/North America/United States/New York"
    ],
    [
      "plos",
      "/People and places/Population groupings/Sexuality groupings/Men who have sex with men"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
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  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1013987&type=printable",
  "pmcid": "PMC13686288",
  "pmid": "42868193",
  "published": "2026-10-13",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/2jz55aq8cg454.ris",
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  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
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    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
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    {
      "field": "medicine",
      "origin": "plos:infectious diseases",
      "subfield": "infectious-disease"
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  "title": "Forecasting of infectious disease epidemics in sexual networks using attenuated renewal processes",
  "title_text": "Forecasting of infectious disease epidemics in sexual networks using attenuated renewal processes",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:22:28Z",
  "url": "https://research.draconitic.com/paper/2jz55aq8cg454",
  "venue": "PLOS Computational Biology",
  "versions": []
}