The prevalence of onchocerciasis in Africa and Yemen, 2000–2018: a geospatial analysis
Bibliographic record
Abstract
BACKGROUND: Onchocerciasis is a disease caused by infection with Onchocerca volvulus, which is transmitted to humans via the bite of several species of black fly, and is responsible for permanent blindness or vision loss, as well as severe skin disease. Predominantly endemic in parts of Africa and Yemen, preventive chemotherapy with mass drug administration of ivermectin is the primary intervention recommended for the elimination of its transmission. METHODS: A dataset of 18,116 geo-referenced prevalence survey datapoints was used to model annual 2000-2018 infection prevalence in Africa and Yemen. Using Bayesian model-based geostatistics, we generated spatially continuous estimates of all-age 2000-2018 onchocerciasis infection prevalence at the 5 × 5-km resolution as well as aggregations to the national level, along with corresponding estimates of the uncertainty in these predictions. RESULTS: As of 2018, the prevalence of onchocerciasis infection continues to be concentrated across central and western Africa, with the highest mean estimates at the national level in Ghana (12.2%, 95% uncertainty interval [UI] 5.0-22.7). Mean estimates exceed 5% infection prevalence at the national level for Cameroon, Central African Republic, Democratic Republic of the Congo (DRC), Guinea-Bissau, Sierra Leone, and South Sudan. CONCLUSIONS: Our analysis suggests that onchocerciasis infection has declined over the last two decades throughout western and central Africa. Focal areas of Angola, Cameroon, the Democratic Republic of the Congo, Ethiopia, Ghana, Guinea, Mali, Nigeria, South Sudan, and Uganda continue to have mean microfiladermia prevalence estimates exceeding 25%. At and above this level, the continuation or initiation of mass drug administration with ivermectin is supported. If national programs aim to eliminate onchocerciasis infection, additional surveillance or supervision of areas of predicted high prevalence would be warranted to ensure sufficiently high coverage of program interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".