{
  "annotation_id": "c4e0885a-bbaa-4c73-b94e-4b1a2eb21a1e",
  "date_created": "2026-02-17T05:53:23.004000Z",
  "date_modified": "2026-02-17T05:53:23.004000Z",
  "file_hash": "fb425366cb9b5683d5cfdd46f3b6051e295225aff185a42cc407ee31c590d9f0",
  "private": false,
  "record": {
    "abstract": "We investigate the spatial organization of dengue cases in the city of Recife, Brazil, from 2015 to 2024, using tools from statistical physics and topological data analysis. Reported cases are modeled as point clouds in a metric space, and their spatial connectivity is studied through Vietoris-Rips filtrations and zero-dimensional persistent homology, which captures the emergence and collapse of connected components across spatial scales. By parametrizing the filtration using percentiles of the empirical distance distribution, we identify critical percolation thresholds associated with abrupt growth of the largest connected component. These thresholds define distinct geometric regimes, ranging from fragmented spatial patterns to highly concentrated, percolated structures. Remarkably, years with similar incidence levels exhibit qualitatively different percolation behavior, demonstrating that case counts alone do not determine the spatial organization of transmission. Our analysis further reveals pronounced temporal heterogeneity in the percolation properties of dengue spread, including a structural rupture in 2020 characterized by delayed or absent spatial percolation. These findings highlight percolation-based topological observables as physically interpretable and sensitive descriptors of urban epidemic structure, offering a complementary perspective to traditional spatial and epidemiological analyses.",
    "arxiv_id": "2601.09747",
    "authors": [
      "Marc\u00edlio Ferreira dos Santos",
      "Cleiton de Lima Ricardo"
    ],
    "categories": [
      "q-bio.PE",
      "math.GT",
      "stat.AP"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Topological Percolation in Urban Dengue Transmission: A Multi-Scale Analysis of Spatial Connectivity",
    "url": "https://arxiv.org/abs/2601.09747",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "16e9d805-52a0-4bc8-874e-c038485a37a3",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}