Marburg Virus Disease in Rwanda: The Role of Infection Prevention and Control in Reducing Transmission of Infectious Disease Outbreaks among Healthcare Professionals
Bibliographic record
Abstract
Rwanda's first Marburg Virus Disease (MVD) outbreak resulted in 58 infections and 13 deaths within the first two weeks. Over 70% of cases occurred among healthcare professionals, highlighting the vulnerability of frontline workers and exposing critical gaps in the country's healthcare system, particularly in infection prevention and control (IPC) practices. Healthcare workers are essential to sustaining functional healthcare systems. However, they face a higher risk of infection and death at the onset of outbreaks, potentially due to lapses in IPC practices, thereby weakening the healthcare system. Routine and strict adherence to IPC measures would have protected healthcare workers and prevented the transmission of both known and emerging diseases. Thus far, Rwanda has successfully implemented containment strategies such as early detection, contact tracing, and isolation. However, this article argues that long-term investment in IPC protocols is essential to safeguard healthcare workers and ensure system resilience. Strengthening IPC measures and fostering a culture of safety are vital steps toward building a healthcare system capable of effectively managing future infectious disease outbreaks. This perspective article aims to raise awareness about the role of infection prevention and control in reducing the transmission of infectious disease outbreaks among healthcare professionals, motivated by the devastating consequences of the MVD outbreak on the health workforce in Rwanda.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".