2017 Outbreak of Ebola Virus Disease in Northern Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: In 2017, the Democratic Republic of the Congo (DRC) recorded its eighth Ebola virus disease (EVD) outbreak, approximately 3 years after the previous outbreak. METHODS: Suspect cases of EVD were identified on the basis of clinical and epidemiological information. Reverse transcription-polymerase chain reaction (RT-PCR) analysis or serological testing was used to confirm Ebola virus infection in suspected cases. The causative virus was later sequenced from a RT-PCR-positive individual and assessed using phylogenetic analysis. RESULTS: Three probable and 5 laboratory-confirmed cases of EVD were recorded between 27 March and 1 July 2017 in the DRC. Fifty percent of cases died from the infection. EVD cases were detected in 4 separate areas, resulting in > 270 contacts monitored. The complete genome of the causative agent, a variant from the Zaireebolavirus species, denoted Ebola virus Muyembe, was obtained using next-generation sequencing. This variant is genetically closest, with 98.73% homology, to the Ebola virus Mayinga variant isolated from the first DRC outbreaks in 1976-1977. CONCLUSION: A single spillover event into the human population is responsible for this DRC outbreak. Human-to-human transmission resulted in limited dissemination of the causative agent, a novel Ebola virus variant closely related to the initial Mayinga variant isolated in 1976-1977 in the DRC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".