{
  "abstract": "Efforts to predict zoonotic disease dynamics rarely directly quantify the fine-scale animal-human interactions that drive spillover. Public digital data (i.e., iEcology) could capture these behaviours at large scales, across time and space. Further, recent statistical advances allow us to semi-automate these processes, providing a framework for continuous exposure monitoring and risk prediction. Using online reviews describing macaque behaviour across southeast Asia, we demonstrate that iEcology provides a usable data source for risky animal-human interactions. We discuss challenges in operationalising these approaches, including ethical considerations and correcting spatial, taxonomic, and socioeconomic bias. In the Big Data era, semi-automated workflows like these could pave the way to dynamically updating risk maps for use in conservation and public health planning and interventions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Bríon Ó Conchubhair"
    },
    {
      "name": "Maxwell J Farrell"
    },
    {
      "name": "Rory Gibb"
    },
    {
      "name": "Luci Kirkpatrick"
    },
    {
      "name": "David W. Redding"
    },
    {
      "name": "Gregory F Albery"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ajth9t7gehcdd.bib",
  "cited_by": 0,
  "doi": "10.32942/x2b38s",
  "fulltext_url": null,
  "id": "ajth9t7gehcdd",
  "issns": [],
  "landing_url": "https://ecoevorxiv.org/repository/view/15136/",
  "language": null,
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Animal Behaviour"
    ],
    [
      "bepress",
      "Behavioural Ecology"
    ],
    [
      "bepress",
      "Big Data"
    ],
    [
      "bepress",
      "Digital Epidemiology"
    ],
    [
      "bepress",
      "Human-Animal Contact"
    ],
    [
      "bepress",
      "Life Sciences"
    ],
    [
      "bepress",
      "Risk Prediction"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Zoonotic Disease"
    ],
    [
      "bepress",
      "Zoonotic Disease; Animal Behaviour; Big Data; iEcology; Digital Epidemiology; Human-Animal Contact; Behavioural Ecology; Risk Prediction"
    ],
    [
      "bepress",
      "iEcology"
    ],
    [
      "openalex",
      "2739|Zoonotic diseases and public health"
    ]
  ],
  "openalex": "W7214028211",
  "pdf_url": "https://ecoevorxiv.org/repository/object/15136/download/26257/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ajth9t7gehcdd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "15136",
      "source": "ecoevorxiv",
      "url": "https://ecoevorxiv.org/repository/view/15136/"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2739|Zoonotic diseases and public health",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Mining online behavioural data to measure zoonotic spillover risk",
  "title_text": "Mining online behavioural data to measure zoonotic spillover risk",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/ajth9t7gehcdd",
  "venue": "EcoEvoRxiv",
  "versions": []
}