Quantifying the Evolving Contribution of HIV Interventions and Key Populations to the HIV Epidemic in Yaoundé, Cameroon
Bibliographic record
Abstract
BACKGROUND: Key populations (KP) including men who have sex with men (MSM), female sex workers (FSW), and their clients are disproportionately affected by HIV in Sub-Saharan Africa. We estimated the evolving impact of past interventions and contribution of unmet HIV prevention/treatment needs of key populations and lower-risk groups to HIV transmission. SETTING: Yaoundé, Cameroon. METHODS: We parametrized and fitted a deterministic HIV transmission model to Yaoundé-specific demographic, behavioral, HIV, and intervention coverage data in a Bayesian framework. We estimated the fraction of incident HIV infections averted by condoms and antiretroviral therapy (ART) and the fraction of all infections over 10-year periods directly and indirectly attributable to sex within and between each risk group. RESULTS: Condom use and ART together may have averted 43% (95% uncertainty interval: 31-54) of incident infections over 1980-2018 and 72% (66-79) over 2009-2018. Most onward transmissions over 2009-2018 stemmed from sex between lower-risk individuals [47% (32-61)], clients [37% (23-51)], and MSM [35% (20-54)] with all their partners. The contribution of commercial sex decreased from 25% (8-49) over 1989-1998 to 8% (3-22) over 2009-2018, due to higher intervention coverage among FSW. CONCLUSION: Condom use and recent ART scale-up mitigated the HIV epidemic in Yaoundé and changed the contribution of different partnerships to onward transmission over time. Findings highlight the importance of prioritizing HIV prevention and treatment for MSM and clients of FSW whose unmet needs now contribute most to onward transmission, while maintaining services that successfully reduced transmissions in the context of commercial sex.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".