Exploring the national burden and challenges in the fight against yellow fever in the Democratic Republic of Congo: a review
Bibliographic record
Abstract
The Democratic Republic of Congo (DR Congo) is facing a public health emergency due to numerous infectious diseases, predominately yellow fever. Since 2015, numerous outbreaks of the illness have occurred in the country which resulted in detrimental impacts on the population. As of February 2024, the DR Congo has reported over 1,200 suspected yellow fever cases with an 11% case fatality rate. This represents a 22% increase compared to 2021 when 203 confirmed cases were reported with a 9% fatality rate. Although there is no specific medication to treat yellow fever, vaccination is proven to be the most effective method of prevention. Despite national and international efforts to combat the disease through vaccination campaigns, yellow fever continues to pose a significant threat. This is because vaccination efforts are limited by the inadequate infrastructure, poverty, poor sanitation and the presence of rebel groups in the DR Congo. Early diagnosis, the use of mosquito nets and insecticides, as well as raising awareness can furthermore aid in limiting transmission. This review explores the prevalence, diagnosis and prevention methods of yellow fever in the DR Congo, as well as the numerous obstacles faced by the country to eliminate it.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".