Environmental Contamination and Persistence of Ebola Virus RNA in an Ebola Treatment Center
Bibliographic record
Abstract
BACKGROUND: Ebola viruses (EBOVs) are primarily transmitted by contact with infected body fluids. Ebola treatment centers (ETCs) contain areas that are exposed to body fluids through the care of patients suspected or confirmed to have EBOV disease. There are limited data documenting which areas/fomites within ETCs pose a risk for potential transmission. This study conducted environmental surveillance in 2 ETCs in Freetown, Sierra Leone, during the 2014-2016 West African Ebola outbreak. METHODS: ETCs were surveyed over a 3-week period. Sites to be swabbed were identified with input from field personnel. Swab samples were collected and tested for the presence of EBOV RNA. Ebola-positive body fluid-impregnated cotton pads were serially sampled. RESULTS: General areas of both ETCs were negative for EBOV RNA. The immediate vicinity of patients was the area most likely to be positive for EBOV RNA. Personal protective equipment became positive during patient care, but chlorine solution washes rendered them negative. CONCLUSIONS: Personal protective equipment and patient environs do become positive for EBOV RNA, but careful attention to decontamination seems to remove it. EBOV RNA was not detected in general ward spaces. Careful attention to decontamination protocols seems to be important in minimizing the presence of EBOV RNA within ETC wards.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".