The impact of out-migrants and out-migration on the HIV/AIDS epidemic: a case study from south-west India
Bibliographic record
Abstract
OBJECTIVE: Seasonal migration may be an important driver of the HIV epidemic in India; however, migrant sexual behaviour data are limited. This study assessed the extent to which migration could explain heterogeneity in HIV prevalence in Bagalkot district, in Karnataka state, India, examining important migration-related risk factors for HIV transmission and implications for prevention. DESIGN: We used mathematical modelling to explore the potential impact of different seasonal migration patterns on HIV prevalence. METHODS: A deterministic compartmental mathematical model of heterosexually transmitted HIV infection was developed. Six migration scenarios were explored, depending on which population migrated (men/clients only/female sex workers; FSW), and which local population determined the demand for commercial sex while migrants were away. RESULTS: The impact of migration varied substantially across the six migration scenarios. Migration was unlikely to explain heterogeneity in HIV prevalence unless a fraction of all men migrated and local FSW drove the demand for commercial sex. Even with very high-risk migrant sexual behaviour in the migration destination, targeting interventions at 30%-100% of local core groups could prevent a maximum of 12%-40% of new infections (87% effective condoms), from 2004-2015. Targeting migrants locally and at their destination could have up to 1.6-times the impact of targeting migrants only at their destination. CONCLUSIONS: Results suggest that core group interventions introduced locally because of the difficulty of reaching migrant populations could still be beneficial. Understanding how local sexual networks change during migration is crucial for understanding the impact of migration on HIV transmission, and for designing HIV preventive interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".