Couple serostatus patterns in sub-Saharan Africa illuminate the relative roles of transmission rates and sexual network characteristics in HIV epidemiology
Bibliographic record
Abstract
HIV prevalence has surpassed 30% in some African countries while peaking at less than 1% in others. The extent to which this variation is driven by biological factors influencing the HIV transmission rate or by variation in sexual network characteristics remains widely debated. Here, we leverage couple serostatus patterns to address this question. HIV prevalence is strongly correlated with couple serostatus patterns across the continent; in particular, high prevalence countries tend to have a lower ratio of serodiscordancy to concordant positivity. To investigate the drivers of this continental pattern, we fit an HIV transmission model to Demographic and Health Survey data from 45,041 cohabiting couples in 25 countries. In doing so, we estimated country-specific HIV transmission rates and sexual network characteristics reflective of pre-couple and extra-couple sexual contact patterns. We found that variation in the transmission rate could parsimoniously explain between-country variation in both couple serostatus patterns and prevalence. In contrast, between-country variation in pre-couple or extra-couple sexual contact rates could not explain the observed patterns. Sensitivity analyses suggest that future work should examine the robustness of this result to between-country variation in how heterogeneous infection risk is within a country, or to assortativity, i.e. the extent to which individuals at higher risk are likely to partner with each other.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".