{
  "abstract": "Using national surveillance records, we evaluated whether COVID-19 dynamics explain, anticipate, or improve prediction of weekly dengue surveillance-record burden in Mexico during 2020–2025. We analysed 1,085,869 dengue records and 20,632,470 COVID-19 records. Audit of daily symptom-onset coverage identified 10 incomplete dengue weeks clustered around year-end/source snapshot boundaries; these weeks were treated as missing rather than zero, leaving 8,695 state-week observations from 32 entities for inferential analyses. Primary weekly counts were transformed as log10(records + 1), with a population-standardized sensitivity analysis using fixed 2020 INEGI populations. We examined pooled, year-specific, pandemic/post-pandemic, state-specific seasonal, nonlinear time-adjusted and lagged associations, and performed a direct predictive ablation. The audited pooled association was inverse (Pearson r = -0.331, bootstrap 95% CI -0.349 to -0.312; Spearman rho = -0.311), but its direction and magnitude varied by year, from r = 0.096 in 2021 to r = -0.312 in 2023. Population standardization yielded r = -0.386, whereas removal of both state and calendar-week effects reduced the association to r = -0.049. After state-specific adjustment for year, cyclic epidemiological-week structure and nonlinear time, the pooled residual correlation was r = -0.055; after additional AR(4) prewhitening, the largest absolute lag correlation was 0.053. In the direct ablation, adding COVID-19 lags to dengue lags, seasonal week and state effects changed test RMSElog10 from 0.1753 to 0.1749 (delta = -0.0004; block-bootstrap 95% interval − 0.0019 to 0.0011). These findings support no stable direct temporal or predictive coupling between the two surveillance series; the pooled inverse association is primarily a feature of calendar-time and epidemic-regime structure.",
  "abstract_shown": true,
  "affiliations": [
    "Escuela Politécnica Superior, Universidad Católica de Murcia (UCAM)",
    "Universidad Internacional de Valencia",
    "Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada (CICATA), Unidad Morelos del Instituto Politécnico Nacional (IPN)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Igor Garcia-Atutxa"
    },
    {
      "affs": [
        1
      ],
      "name": "Carmen Luna Cruz-Herrero"
    },
    {
      "affs": [
        2
      ],
      "name": "Francisca Villanueva-Flores"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d63nh3d4rm6v5.bib",
  "cited_by": 0,
  "doi": "10.1186/s12982-026-02929-4",
  "fulltext_url": "https://doi.org/10.1186/s12982-026-02929-4",
  "id": "d63nh3d4rm6v5",
  "issns": [
    "3005-0774"
  ],
  "landing_url": "https://doi.org/10.1186/s12982-026-02929-4",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RA1-1270"
    ],
    [
      "openalex",
      "2739|Mosquito-borne diseases and control"
    ]
  ],
  "openalex": "W7223097706",
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s12982-026-02929-4.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/d63nh3d4rm6v5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:39Z",
      "native_id": "e280eea210d84134bb0342e9c9867b9c",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s12982-026-02929-4"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2739|Mosquito-borne diseases and control",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "A nationwide ecological time-series analysis found no stable temporal or predictive relationship between dengue and COVID-19 surveillance records in Mexico from 2020 to 2025",
  "title_text": "A nationwide ecological time-series analysis found no stable temporal or predictive relationship between dengue and COVID-19 surveillance records in Mexico from 2020 to 2025",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:39Z",
  "url": "https://research.draconitic.com/paper/d63nh3d4rm6v5",
  "venue": "Discover Public Health",
  "versions": []
}