Targeting HIV services to male migrant workers in southern Africa would not reverse generalized HIV epidemics in their home communities: a mathematical modeling analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Migrant populations such as mine workers contributed to the spread of HIV in sub-Saharan Africa. We used a mathematical model to estimate the community-wide impact of targeting treatment and prevention to male migrants. METHODS: We augmented an individual-based network model, EMOD-HIV v0.8, to include an age-dependent propensity for males to migrate. Migrants were exposed to HIV outside their home community, but continued to participate in HIV transmission in the community during periodic visits. RESULTS: Migrant-targeted interventions would have been transformative in the 1980s to 1990s, but post-2015 impacts were more modest. When targetable migrants comprised 2% of adult males, workplace HIV prevention averted 3.5% of community-wide infections over 20 years. Targeted treatment averted 8.5% of all-cause deaths among migrants. When migrants comprised 10% of males, workplace prevention averted 16.2% of infections in the community, one-quarter of which were among migrants. Workplace prevention and treatment acted synergistically, averting 17.1% of community infections and 11.6% of deaths among migrants. These estimates do not include prevention of secondary spread of HIV or tuberculosis at the workplace. CONCLUSIONS: Though cost-effective, targeting migrants cannot collapse generalized epidemics in their home communities. Such a strategy would only have been possible prior to the early 1990s. However, migrant-targeted interventions synergize with general-population expansion of HIV services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it