{
  "abstract": "Background\n\nEarly clinical diagnosis of Ebola virus disease (EVD) is challenging in outbreak settings due to symptom overlap with malaria. We assessed whether a Bayesian subjective model (BSM) outperforms logistic regression model (LRM) in improving Ebola screening.Methods\n\nThe BSM, incorporating seven expert-identified predictive factors (hemorrhagic, neurological, digestive, pain, general symptoms, epidemiological link, respiratory), underwent internal and external validation using data from 450 patients triaged in the Democratic Republic of Congo. Diagnostic performance metrics (Sensitivity (Se), Specificity (Sp), Positive Predictive Value (PPV), Negative Predictive Value (NPV), overall accuracy) and likelihood ratios (LR) from BSM were compared to those of LRM. Significant odds ratios (OR) (95% CI excluding 1; p < 0.05) were identified via logistic regression analysis. Microsoft Excel and SPSS were utilized for statistical analyses.Results\n\nExperts identified seven predictive factors for Ebola, with four (hemorrhagic, neurological, digestive signs, and epidemiological contact; L.R > 1.5) showing strong likelihood ratios (Table 3). Patients presenting both hemorrhagic signs and epidemiological link had a 60.5% probability of confirmed Ebola. The BSM exhibited higher sensitivity (84.2% vs. 71.7%) compared to LRM under real-world conditions. Significant predictors included hemorrhagic signs (OR 0.13, 95% CI 0.07-0.23, p < 0.001), pain signs (OR 2.06, 95% CI 1.19-3.58, p = 0.01), general signs (OR 3.92, 95% CI 1.76-8.73, p < 0.001), and epidemiological link (OR 0.54, 95% CI 0.30-0.96, p = 0.04); their confidence intervals excluded 1, confirming statistical significance.Conclusions\n\nThe BSM offers diagnostic superiority in triage of EVD cases, particularly in resource-limited outbreak settings. Further multicenter studies incorporating biological data are recommended to enhance its robustness and operational validity.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, BP 11850 Kin I, Democratic Republic of Congo. john.kamwina@unikin.ac.cd.",
    "Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, BP 11850 Kin I, Democratic Republic of Congo.",
    "Department of Management, School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of Congo.",
    "Department of Mathematics and Computer Science, Faculty of Science, University of Kinshasa, Kinshasa, Democratic Republic of Congo.",
    "Educational and Vocational Guidance Department, Ilebo Higher Pedagogical Institute, Ilebo, Democratic Republic of Congo.",
    "Department of Anaesthesia and Intensive Care, University Clinics, University of Kinshasa, Kinshasa, Democratic Republic of Congo.",
    "Department of Public Health Emergencies and Operations Center (NIPH/PHEOC), National Institute of Public Health, Kinshasa, Democratic Republic of Congo."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "John Kamwina Kebela"
    },
    {
      "affs": [
        1
      ],
      "name": "Prince Kimpanga"
    },
    {
      "affs": [
        1
      ],
      "name": "Jean Nyandwe"
    },
    {
      "affs": [
        1
      ],
      "name": "Jack Kokolomami"
    },
    {
      "affs": [
        2
      ],
      "name": "Steve Bwira"
    },
    {
      "affs": [
        3
      ],
      "name": "Rostin Mabela"
    },
    {
      "affs": [
        4
      ],
      "name": "Olivier Mangapi"
    },
    {
      "affs": [
        5
      ],
      "name": "Berthe Barhayiga"
    },
    {
      "affs": [
        1
      ],
      "name": "Godfroid Musema"
    },
    {
      "affs": [
        6
      ],
      "name": "Bienvenu Kabasele"
    },
    {
      "affs": [
        2
      ],
      "name": "Sylvain Munyanga"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d8cs4az6q5c8v.bib",
  "cited_by": 0,
  "doi": "10.1186/s13000-026-01825-4",
  "fulltext_url": "https://europepmc.org/articles/PMC13729609",
  "id": "d8cs4az6q5c8v",
  "issns": [
    "1746-1596"
  ],
  "landing_url": "https://europepmc.org/article/MED/42972355",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2725|Viral Infections and Outbreaks Research"
    ]
  ],
  "openalex": "W4404829939",
  "pdf_url": "https://europepmc.org/articles/PMC13729609?pdf=render",
  "pmcid": "PMC13729609",
  "pmid": "42972355",
  "published": "2026-08-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d8cs4az6q5c8v.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:20:49Z",
      "native_id": "MED:42972355",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42972355"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2725|Viral Infections and Outbreaks Research",
      "subfield": "infectious-disease"
    }
  ],
  "title": "Development of a Bayesian subjective model for predicting the clinical diagnosis of Ebola in the Democratic Republic of the Congo.",
  "title_text": "Development of a Bayesian subjective model for predicting the clinical diagnosis of Ebola in the Democratic Republic of the Congo.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T03:21:01Z",
  "url": "https://research.draconitic.com/paper/d8cs4az6q5c8v",
  "venue": "Diagnostic pathology",
  "versions": []
}