{
  "abstract": "Malaria transmission models can generate precise intervention rankings even when their parameters, observation mappings and programme costs remain uncertain. I evaluated an age-stratified SEIR-SEI framework in a standardised population of one million, using an unchanged 36-month historical synthetic target. The reconciled implementation maintained virtual age-group totals, fitted the transmission composite K = {beta}M, integrated infectious onsets and disease deaths, and compared interventions from one phase-aligned baseline state over 720 days. An aggregate vaccine effect entered child infection before simulation, allowing transmission feedback.\n\nFive calibration seeds gave total-series R{superscript 2} 0.9537; subgroup fits were weaker. The closed-form unforced reproduction index, 6.3787, matched the six-state next-generation matrix. The baseline produced 528,041 expected reported episodes. ITN coverage of 50% averted 36,692 episodes; vaccine coverage of 70% averted 2,123 under the assumed aggregate mechanism. At a hypothetical US$2 million additional prevention budget, five allocation seeds and a 10,201-point boundary grid selected 71.43% ITN coverage, averting 52,482.83 reported episodes. The same corner won within a 115-portfolio pool for all 300 assumed parameter draws and remained selected after 40 repeated noisy-target recalibrations. Halving the IRS round price and using two rounds changed the selected mix to 40.07% ITN and 21.95% IRS. All 17 mathematical and numerical assertions passed.\n\nThe study demonstrates reproducible conditional calculations and identifies important structural confounding. It does not establish empirical Uganda programme effectiveness, a clinical treatment allocation or a national cost-effectiveness recommendation. Empirical use requires compatible surveillance observations, a cohort-based vaccine mechanism, treatment-specific clinical effects and locally measured programme resources.",
  "abstract_shown": true,
  "affiliations": [
    "Busitema university"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. H. Patel"
    },
    {
      "name": "J. Mulangwa"
    },
    {
      "name": "M. Musaba"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2kd5g3fffj8r5.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.23.26365767",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365767v1.full",
  "id": "2kd5g3fffj8r5",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365767v1",
  "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",
      "2739|Malaria Research and Control"
    ]
  ],
  "openalex": "W7223440758",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365767v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2kd5g3fffj8r5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:40Z",
      "native_id": "10.64898/2026.10.23.26365767",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365767v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Malaria transmission modelling and conditional prevention allocation in Uganda",
  "title_text": "Malaria transmission modelling and conditional prevention allocation in Uganda",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:40Z",
  "url": "https://research.draconitic.com/paper/2kd5g3fffj8r5",
  "venue": "medRxiv",
  "versions": []
}