The Use of a Mobile Laboratory Unit in Support of Patient Management and Epidemiological Surveillance during the 2005 Marburg Outbreak in Angola
Bibliographic record
Abstract
BACKGROUND: Marburg virus (MARV), a zoonotic pathogen causing severe hemorrhagic fever in man, has emerged in Angola resulting in the largest outbreak of Marburg hemorrhagic fever (MHF) with the highest case fatality rate to date. METHODOLOGY/PRINCIPAL FINDINGS: A mobile laboratory unit (MLU) was deployed as part of the World Health Organization outbreak response. Utilizing quantitative real-time PCR assays, this laboratory provided specific MARV diagnostics in Uige, the epicentre of the outbreak. The MLU operated over a period of 88 days and tested 620 specimens from 388 individuals. Specimens included mainly oral swabs and EDTA blood. Following establishing on site, the MLU operation allowed a diagnostic response in <4 hours from sample receiving. Most cases were found among females in the child-bearing age and in children less than five years of age. The outbreak had a high number of paediatric cases and breastfeeding may have been a factor in MARV transmission as indicated by the epidemiology and MARV positive breast milk specimens. Oral swabs were a useful alternative specimen source to whole blood/serum allowing testing of patients in circumstances of resistance to invasive procedures but limited diagnostic testing to molecular approaches. There was a high concordance in test results between the MLU and the reference laboratory in Luanda operated by the US Centers for Disease Control and Prevention. CONCLUSIONS/SIGNIFICANCE: The MLU was an important outbreak response asset providing support in patient management and epidemiological surveillance. Field laboratory capacity should be expanded and made an essential part of any future outbreak investigation.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".