{
  "abstract": "BackgroundWaning vaccine protection and variable epidemic timing create a tradeoff between vaccinating before influenza circulation and preserving protection when disease burden is greatest. Existing guidance does not quantify the week that best balances this tradeoff.\n\nObjectiveTo estimate the optimal week for influenza vaccination while accounting for uncertainty in epidemic timing and vaccine protection.\n\nDesignSimulation-based probabilistic decision analysis.\n\nSettingUnited States outpatient influenza surveillance from the 50 states and District of Columbia during the 2010/11 through 2018/19 and 2023/24 through 2025/26 seasons.\n\nParticipantsPeople in the United States who would receive 1 influenza vaccine dose.\n\nInterventionsVaccination during Morbidity and Mortality Weekly Report (MMWR) weeks 36 through 12.\n\nMeasurementsWe combined influenza-like illness (ILI) syndromic surveillance and influenza-specific data with uncertainty in epidemic timing, initial vaccine effectiveness, waning, and immune-response lag. For each candidate week, the model estimated direct protection against outpatient surveillance burden.\n\nResultsThe primary model identified the optimal time to vaccinate as week 47, with weeks 47-48 being of similar utility. Our influenza-weighted outpatient analysis (ILI+) selected week 48. Compared with week 44, near the end of the current September-October guidance window, week 48 reduced modeled influenza-weighted outpatient burden by 50 visit equivalents per 10,000 weekly encounters.\n\nLimitationsThe model assumed vaccination would occur, used outpatient surveillance burden rather than severe outcomes, and did not incorporate transmission effects.\n\nConclusionUnder the primary waning model, these findings favor vaccinating during weeks 47-48, approximately late November through early December, for most one-dose recipients.\n\nPrimary Funding SourceNone.",
  "abstract_shown": true,
  "affiliations": [
    "Baylor College of Medicine"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. G. Meyer"
    },
    {
      "name": "B. N. Rosales"
    },
    {
      "name": "S. Yang"
    },
    {
      "name": "M. Santillana"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/92gy6q7szeard.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.11.26362843",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362843v1.full",
  "id": "92gy6q7szeard",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362843v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "epidemiology"
    ],
    [
      "openalex",
      "2713|Influenza Virus Research Studies"
    ]
  ],
  "openalex": "W7212457291",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362843v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/92gy6q7szeard.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:28Z",
      "native_id": "10.64898/2026.09.11.26362843",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362843v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Decision Analysis Modeling Favors Optimal Influenza Vaccination in Late November or Early December",
  "title_text": "Decision Analysis Modeling Favors Optimal Influenza Vaccination in Late November or Early December",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:28Z",
  "url": "https://research.draconitic.com/paper/92gy6q7szeard",
  "venue": "medRxiv",
  "versions": []
}