Refining early detection of Marburg Virus Disease (MVD) in Rwanda: Leveraging predictive symptom clusters to enhance case definitions
Bibliographic record
Abstract
BACKGROUND: Marburg Virus Disease (MVD) poses a significant global health risk due to its high case fatality rates (24%-88%) and the diagnostic challenges posed by its nonspecific early symptoms, which overlap with other febrile illnesses like malaria. This study analyzed symptom patterns from the 2024 MVD outbreak in Rwanda to refine case definitions and enhance early detection. METHODS: A retrospective analysis was conducted of 6613 suspected MVD cases (66 positive, 6547 negative) reported between September 27 and December 20, 2024. Symptom prevalence and predictive value were assessed using multiple logistic regression models with L1 and L2 regularization to identify the most predictive symptoms. Models were validated using 5-fold cross-validation, with performance assessed through ROC analysis and standard accuracy metrics. RESULTS: Fever (78.8%), fatigue (63.6%), and headache (57.6%) were identified as the most common early symptoms, while hemorrhagic signs were rare (3.0%). The model achieved high accuracy (99.04%) and an area under the receiver operating characteristic curve of 0.824, identifying fever, fatigue, nausea/vomiting, joint pain, and sore throat as key predictors. CONCLUSION: Early symptom clusters, especially constitutional and gastrointestinal signs outperformed hemorrhagic symptoms for MVD detection. Findings challenge current case definitions, emphasizing the need for revised public health messaging and healthcare worker training. Integrating symptom-based models into surveillance could enhance detection, especially in resource-limited settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".