{
  "abstract": "Accurate representations of origin-destination (OD) passenger flows on the World Air Transportation Network (WAN) are fundamental inputs to models of global mobility, epidemic risk, and infrastructure planning. However, OD data are largely commercial and proprietary, therefore often inaccessible to the research community. Here we introduce a generative model that treats air mobility as a stochastic process within a sparse maximum-entropy framework, informed by aggregate traffic volumes of individual locations and geographic regions. The model accurately reconstructs both network structure and mobility flows, yielding epidemic spreading trajectories that closely match those obtained using real mobility data. Furthermore, the method can be effectively implemented using open data on flight routes and airport traffic, reducing dependence on proprietary datasets. Our approach provides a scalable, interpretable, and computationally efficient framework for scenario analysis and policy design in global mobility systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Giulia Fischetti",
      "orcid": "0000-0002-2916-7678"
    },
    {
      "name": "Anna Mancini",
      "orcid": "0000-0002-0705-1252"
    },
    {
      "name": "Giulio Cimini",
      "orcid": "0000-0001-5698-8597"
    },
    {
      "name": "Jessica T. Davis"
    },
    {
      "name": "Abby O. K. Leung"
    },
    {
      "name": "Alessandro Vespignani"
    },
    {
      "name": "Guido Caldarelli",
      "orcid": "0000-0001-9377-3616"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/72eqee2mq7fy3.bib",
  "cited_by": 0,
  "doi": "10.1038/s41467-026-78115-6",
  "fulltext_url": null,
  "id": "72eqee2mq7fy3",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://www.nature.com/articles/s41467-026-78115-6",
  "language": null,
  "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": [
    [
      "openalex",
      "3109|Complex Network Analysis Techniques"
    ]
  ],
  "openalex": "W7125234401",
  "pdf_url": "https://www.nature.com/articles/s41467-026-78115-6_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/72eqee2mq7fy3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:38Z",
      "native_id": "10.1038/s41467-026-78115-6",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41467-026-78115-6"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3109|Complex Network Analysis Techniques",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Block-fitness modeling of the global air mobility network",
  "title_text": "Block-fitness modeling of the global air mobility network",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:38Z",
  "url": "https://research.draconitic.com/paper/72eqee2mq7fy3",
  "venue": "Nature Communications",
  "versions": []
}