{
  "abstract": "Predictive modelling for infectious diseases remains an important problem in epidemiology. Current approaches are often limited by their dependence on time-series data, which can lead to biased estimates of key epidemiological parameters. Here, we present an extension of the dynamical survival analysis approach that can accommodate epidemic data from a large-scale, complex, agent-based model. We use localized predictions that allow for flexibility with time-dependent parameters to account for human behaviour and environmental factors during the epidemic. Our results demonstrate that it is possible to evaluate the quality of predictions and highlight the potential benefits of surveillance via longitudinal follow-up of individuals sampled from the population, which we call a human sensor network.",
  "abstract_shown": true,
  "affiliations": [
    "Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA",
    "Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Micaela Richter",
      "orcid": "0009-0006-2543-400X"
    },
    {
      "affs": [
        1
      ],
      "name": "Prescott Alexander"
    },
    {
      "affs": [
        0
      ],
      "name": "Grzegorz Rempala",
      "orcid": "0000-0002-6307-4555"
    },
    {
      "affs": [
        0
      ],
      "name": "Eben Kenah",
      "orcid": "0000-0002-7117-7773"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9z9chyghn8wmm.bib",
  "cited_by": 0,
  "doi": "10.1098/rsif.2026.0111",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13714384/",
  "id": "9z9chyghn8wmm",
  "issns": [
    "1742-5689",
    "1742-5662"
  ],
  "landing_url": "https://doi.org/10.1098/rsif.2026.0111",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Biomathematics"
    ],
    [
      "heading",
      "Computational Biology"
    ],
    [
      "heading",
      "Life Sciences–Mathematics interface"
    ],
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2739|Mathematical and Theoretical Epidemiology and Ecology Models"
    ]
  ],
  "openalex": "W7224111883",
  "pdf_url": "https://royalsocietypublishing.org/rsif/article-pdf/doi/10.1098/rsif.2026.0111/6173666/rsif.2026.0111.pdf",
  "pmcid": "PMC13714384",
  "pmid": "42947370",
  "published": "2026-10-07",
  "publisher": "The Royal Society",
  "ris": "https://research.draconitic.com/paper/9z9chyghn8wmm.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:21:53Z",
      "native_id": "PMC13714384",
      "source": "pmc",
      "url": "https://doi.org/10.1098/rsif.2026.0111"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "heading:Computational Biology",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Local dynamical survival analysis and human sensor networks for epidemic tracking and forecasting",
  "title_text": "Local dynamical survival analysis and human sensor networks for epidemic tracking and forecasting",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:53Z",
  "url": "https://research.draconitic.com/paper/9z9chyghn8wmm",
  "venue": "Journal of the Royal Society Interface",
  "versions": []
}