{
  "annotation_id": "a46b0fb9-7d6c-47b5-b1e4-a4b7bcfe4d18",
  "date_created": "2026-03-02T18:01:11.273000Z",
  "date_modified": "2026-03-02T18:01:11.273000Z",
  "file_hash": "0f564e2edbeeff05557086b79eec2ede1be74f404d54c63f0f5bb766b5b295a4",
  "private": false,
  "record": {
    "abstract": "Many real networks are embedded in a metric space: the interactions among\nindividuals depend on their spatial distances and usually take place among\ntheir nearest neighbors. In this paper, we introduce a modified\nsusceptible-infected-susceptible (SIS) model to study geographical effects on\nthe spread of diseases by assuming that the probability of a healthy individual\ninfected by an infectious one is inversely proportional to the Euclidean\ndistance between them. It is found that geography plays a more important role\nthan hubs in disease spreading: the more geographically constrained the network\nis, the more highly the epidemic prevails.",
    "arxiv_id": "physics/0606256",
    "authors": [
      "Xin-Jian Xu",
      "Wen-Xu Wang",
      "Tao Zhou",
      "Guanrong Chen"
    ],
    "categories": [
      "physics.soc-ph"
    ],
    "doi": "10.1142/S0129183106010194",
    "journal_ref": "Int. J. Mod. Phys. C 17, 1815 (2006).",
    "title": "Geographical effects on epidemic spreading in scale-free networks",
    "url": "https://arxiv.org/abs/physics/0606256"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b2b6cd68-1406-493c-8873-94fbe572d53d",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}