Surface sampling for mpox virus in multiple healthcare settings in Sierra Leone, June 2025
Bibliographic record
Abstract
ABSTRACT Background As mpox virus (MPXV) has continued to expand geographically, critical knowledge gaps in environmental persistence in resource-limited healthcare settings remain. Despite evidence of fomite-mediated transmission, no empirical data exist on surface contamination in West African hospitals during active mpox outbreaks. Methods In this cross-sectional study, we conducted a systematic environmental surveillance study at two major hospitals in Sierra Leone (Connaught Hospital, Freetown; Bo Government Hospital, Bo) during peak transmission (June 2025). A total of 89 high-contact surfaces were sampled across clinical and non-clinical zones using standardized protocols. MPXV DNA was extracted via robotic MagMax protocols and detected through quantitative real-time PCR targeting the B6R gene. Results Overall, 13.5% (12/89) of surfaces tested positive by PCR for MPXV, with geographic variation: Freetown (14.0%, 7/50) vs. Bo (12.8%, 5/39). Cycle threshold values (32.34–39.86) indicated low-to-moderate viral genomes. Critical contamination hotspots were identified, with doors representing 42% (5/12) of positive samples; predominantly ward entrances, staff offices, and bathrooms. Patient beds and clinical instruments constituted secondary risk zones (8.3% each). Conclusions This first-in-region study demonstrates quantifiable MPXV genomes from surfaces in Sierra Leonean healthcare facilities, providing support for patients with mpox, highlighting areas for infection prevention and control (IPC) considerations. The predominance of doors as high-risk fomites underscores the need for targeted disinfection protocols. Our findings establish environmental surveillance as a vital component of mpox control in clinical settings and provide evidence for IPC resource prioritization, including enhanced disinfection of high-touch surfaces and integration of IPC monitoring into national outbreak response frameworks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".