Population-level impact of an accelerated HIV response plan to reach the UNAIDS 90-90-90 target in Côte d’Ivoire: Insights from mathematical modeling
Bibliographic record
Abstract
BACKGROUND: National responses will need to be markedly accelerated to achieve the ambitious target of the Joint United Nations Programme on HIV/AIDS (UNAIDS). This target aims for 90% of HIV-positive individuals to be aware of their status, for 90% of those aware to receive antiretroviral therapy (ART), and for 90% of those on treatment to have a suppressed viral load by 2020, with each individual target reaching 95% by 2030. We aimed to estimate the impact of various treatment-as-prevention scenarios in Côte d'Ivoire, one of the countries with the highest HIV incidence in West Africa, with unmet HIV prevention and treatment needs, and where key populations are important to the broader HIV epidemic. METHODS AND FINDINGS: An age-stratified dynamic model was developed and calibrated to epidemiological and programmatic data using a Bayesian framework. The model represents sexual and vertical HIV transmission in the general population, female sex workers (FSW), and men who have sex with men (MSM). We estimated the impact of scaling up interventions to reach the UNAIDS targets, as well as the impact of 8 other scenarios, on HIV transmission in adults and children, compared to our baseline scenario that maintains 2015 rates of testing, ART initiation, ART discontinuation, treatment failure, and levels of condom use. In 2015, we estimated that 52% (95% credible intervals: 46%-58%) of HIV-positive individuals were aware of their status, 72% (57%-82%) of those aware were on ART, and 77% (74%-79%) of those on ART were virologically suppressed. Reaching the UNAIDS targets on time would avert 50% (42%-60%) of new HIV infections over 2015-2030 compared to 30% (25%-36%) if the 90-90-90 target is reached in 2025. Attaining the UNAIDS targets in FSW, their clients, and MSM (but not in the rest of the population) would avert a similar fraction of new infections (30%; 21%-39%). A 25-percentage-point drop in condom use from the 2015 levels among FSW and MSM would reduce the impact of reaching the UNAIDS targets, with 38% (26%-51%) of infections averted. The study's main limitation is that homogenous spatial coverage of interventions was assumed, and future lines of inquiry should examine how geographical prioritization could affect HIV transmission. CONCLUSIONS: Maximizing the impact of the UNAIDS targets will require rapid scale-up of interventions, particularly testing, ART initiation, and limiting ART discontinuation. Reaching clients of FSW, as well as key populations, can efficiently reduce transmission. Sustaining the high condom-use levels among key populations should remain an important prevention pillar.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".