{
  "abstract": "Background: Monthly malaria forecasts can support diagnostic and treatment planning when the surveillance outcome and evaluation protocol are clearly defined.\n\nMethods: We analysed 4,128 council-month records from 43 councils in six Lake Zone regions during January 2018–December 2025. Complete mRDT-positive counts were aggregated to 576 region-month observations and one 96-month Lake Zone series. Six fitted models, an equal-weight ensemble and a seasonal-naive benchmark were evaluated on a fixed-origin 2025 holdout and 37 expanding-window origins with 1–12-month horizons.\n\nResults: The dataset contained 11,874,519 mRDT-positive counts. Annual counts declined from 1.63 million in 2018 to 1.06 million in 2025, with the highest calendar-month averages in June–July. ARIMA (2, 1, 2) had the lowest 2025 holdout error (MAPE 12.1%; RMSE 11,709), reducing RMSE by 69.2% relative to seasonal naive. Across all rolling origin–horizon pairs, Gradient Boosting had the lowest RMSE (22,007), while the ensemble had the lowest MAE (16,687) and MAPE (16.1%).\n\nConclusions: Model rankings varied with forecast origin, horizon and metric. The findings favour routinely updated, benchmarked forecasts over reliance on a single terminal-year ranking. The outcome represents positive diagnostic counts rather than population-standardized incidence or confirmed unique outpatient episodes.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s41182-026-01105-3.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Science and Management, Dar es Salaam Maritime Institute, P.O. Box 6727, Dar es Salaam, Tanzania",
    "Department of Mathematics, University of Dar es Salaam, P.O. Box 35091, Dar es Salaam, Tanzania",
    "Global Water Parternership Tanzania, P.O. Box 32334, Dar es Salaam, Tanzania"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jonnes James Lugoye",
      "orcid": "0009-0003-6736-4714"
    },
    {
      "affs": [
        0
      ],
      "name": "Theresia Crispin Marijani"
    },
    {
      "affs": [
        0
      ],
      "name": "Idrissa Said Amour"
    },
    {
      "affs": [
        2
      ],
      "name": "Frank Enock Anderson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0hxazxknpn697.bib",
  "cited_by": 0,
  "doi": "10.1186/s41182-026-01105-3",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13741557/",
  "id": "0hxazxknpn697",
  "issns": [
    "1348-8945",
    "1349-4147"
  ],
  "landing_url": "https://doi.org/10.1186/s41182-026-01105-3",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2739|Malaria Research and Control"
    ]
  ],
  "openalex": "W7225434371",
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s41182-026-01105-3.pdf",
  "pmcid": "PMC13741557",
  "pmid": "42995647",
  "published": "2026-11-06",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/0hxazxknpn697.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T12:22:35Z",
      "native_id": "PMC13741557",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s41182-026-01105-3"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2739|Malaria Research and Control",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Time-series forecasting of mRDT-positive malaria counts in Tanzania’s lake zone: a comparative evaluation of statistical and machine-learning models using routine surveillance data, 2018–2025",
  "title_text": "Time-series forecasting of mRDT-positive malaria counts in Tanzania’s lake zone: a comparative evaluation of statistical and machine-learning models using routine surveillance data, 2018–2025",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T12:22:35Z",
  "url": "https://research.draconitic.com/paper/0hxazxknpn697",
  "venue": "Tropical Medicine and Health",
  "versions": []
}