Distinguishing sources of HIV transmission from the distribution of newly acquired HIV infections: why is it important for HIV prevention planning?
Bibliographic record
Abstract
OBJECTIVE: The term 'source of HIV infections' has been referred to as the source of HIV transmission. It has also been interpreted as the distribution of newly acquired HIV infections across subgroups. We illustrate the importance of distinguishing the two interpretations for HIV prevention planning. METHODS: We used a dynamical model of heterosexual HIV transmission to simulate three HIV epidemics, and estimated the sources of HIV transmission (cumulative population attributable fraction) and the single-year distribution of new HIV infections. We focused an intervention guided by the largest transmission source versus the largest single-year distribution of new HIV infections, and compared the fraction of discounted HIV infections averted over 30 years. RESULTS: The single-year distribution of newly acquired HIV infections underestimated the source of HIV transmission in the long term, when the source was unprotected sex in high-risk groups. Under equivalent and finite resources, an intervention strategy directed by the long-term transmission source was shown to achieve a greater impact than a distribution-directed strategy, particularly in the long term. CONCLUSIONS: Impact of HIV prevention strategies may vary depending on whether they are directed by the long-term transmission source or by the distribution of new HIV infections. Caution is required when interpreting the 'source of HIV infections' to avoid misusing the distribution of new HIV infections in HIV prevention planning.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".