What Really Is a Concentrated HIV Epidemic and What Does It Mean for West and Central Africa? Insights From Mathematical Modeling
Bibliographic record
Abstract
BACKGROUND: HIV epidemics have traditionally been classified as "concentrated" among key populations if overall HIV prevalence was below 1% and as "generalized" otherwise. We aimed to objectively determine the utility of this classification by determining how high overall HIV prevalence can reach in epidemics driven by unprotected sex work (SW) and how estimates of the contribution of SW to HIV transmission changes over time in these epidemics. METHODS: We developed a deterministic model of HIV transmission specific to West and Central Africa to simulate 1000 synthetic HIV epidemics, where SW is the sole behavioral driver that sustains HIV in the population (ie, truly concentrated epidemics), and it is based on a systematic extraction of model parameters specific to West and Central Africa. We determined the range of plausible HIV prevalence in the total population over time and calculated the population attributable fraction (PAF) of SW over different time periods. RESULTS: In 1988 and 2008, HIV prevalence across the 1000 synthetic concentrated HIV epidemics ranged (5th-95th percentile) between 0.1%-4.2% and 0.1%-2.8%, respectively. The maximum HIV prevalence peaked at 12%. The PAF of SW measured from 2008 over 1 year was <5%-18% compared with 16%-59% over 20 years in these SW-driven epidemics. CONCLUSIONS: Even high HIV-prevalence epidemics can be driven by unprotected SW and therefore concentrated. Overall, HIV prevalence and the short-term PAF are poor makers of underlying transmission dynamics and underestimate the role of SW in HIV epidemics and thus should not be used alone to inform HIV programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| 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".