P5-S6.28 HIV prevention based on the static modes of transmission synthesis for two Indian districts: insights from dynamical modelling
Bibliographic record
Abstract
Background The Modes of Transmission (MOT) synthesis uses a static HIV transmission model to predict distribution of incident infections along subgroups over 1 year, and directs HIV prevention along this distribution. Because the MOT does not consider where sustained transmission is most likely to occur, and does not use parameter combinations fitted to observed epidemic characteristics, its relevance for planning interventions may be limited. Methods We fitted a dynamical HIV/STI heterosexual transmission model to districts Mysore and Belgaum, India. The MOT and dynamical models estimated the proportion of new HIV infections over 1 year due to transmission between female sex workers/clients, their non-commercial partnerships, and low-risk partnerships. We compared predictions from the dynamical model to MOT results using prior and posterior (fitted) parameters. Intervention impact was illustrated using the dynamical model. Results Using prior inputs, the MOT predicted that commercial sex accounted for 66.2–70.6% of incident infections among males, whereas 71.7–74.2% of incident infections among females were due to bridging infections from clients. There was less variability in MOT results when fitted inputs were used. The majority of the remaining new infections in males and females were due to transmission within low-risk partnerships. In contrast, the dynamical model predicted a higher contribution of commercial sex among males (90.7–91.2%), a higher contribution of bridging infections among females (70.5–86.9%), and that <1.5% of infections were due to low-risk partnerships. Dynamical modelling predicted that any intervention that reduces transmission by 20% applied among commercial sex partnerships could decrease overall HIV incidence by 12% in the first year and by 21% in 5 years see Abstract P5-S6.28 table 1. Applying this intervention among non-commercial partnerships of clients reduces overall incidence by 9% in years 1 through 5 because clients continue to become infected from their commercial partnerships. Abstract P5-S6.28 Table 1 Distribution of 1-year incident infections by type of partnership, as predicted by the MOT and a fitted dynamical model Partnership type Median % of incident infections attributable to partnership types (2.5 and 97.5 percentiles) MOT (prior inputs) MOT (posterior inputs) Dynamical model Mysore Belgaum Mysore Belgaum Mysore Belgaum Females Commercial 6.5 (2.4, 14.9) 5.1 (1.9, 12.2) 7.0 (3.3, 13.0) 4.5 (2.0, 8.8) 28.4 (18.6, 35.5) 11.7 (5.7, 15.0) Main partnerships of FSWs/Clients 71.7 (50.4, 88.7) 74.2 (48.6, 90.9) 75.6 (61.0, 87.8) 73.0 (55.4, 84.9) 70.5 (63.6, 80.3) 86.9 (83.5, 92.9) Casual partnerships of FSWs/Clients 0.03 (0.01, 0.09) 0.01 (0.003, 0.04) 0.02 (0.01, 0.07) 0.01 (0.004, 0.04) 0.30 (0.25, 0.38) 0.31 (0.23, 0.38) Low-risk 20.0 (4.6, 40.7) 19.4 (4.3, 44.3) 15.8 (7.1, 29.3) 21.8 (10.9, 38.7) 0.76 (0.31, 1.1) 1.0 (0.61, 1.4) Males Commercial 70.6 (21.4, 92.6) 66.2 (14.3, 92.8) 66.0 (40.6, 81.1) 69.1 (41.7, 84.3) 91.2 (90.8, 95.6) 90.7 (88.4, 96.1) Main partnerships of FSWs/Clients 9.8 (3.4, 21.3) 10.8 (3.6, 20.9) 11.3 (6.1, 20.2) 9.6 (4.2, 19.5) 6.3 (3.4, 8.1) 7.3 (2.9, 9.2) Casual partnerships of FSWs/Clients 2.0 (0.5, 4.0) 4.5 (1.2, 9.9) 1.7 (0.66, 3.7) 3.8 (1.3, 8.3) 0.5 (0.3, 0.6) 0.87 (0.51, 1.2) Low-risk 15.6 (2.8, 58.5) 13.0 (1.6, 65.8) 19.7 (10.0, 36.9) 14.9 (6.0, 35.4) 0.6 (0.3, 0.9) 1.0 (0.37, 1.4) FSW, female sex worker; MOT, Modes of Transmission. Conclusion Prior inputs for the MOT will not reflect observed HIV prevalence, and as a result, will produce greater variability in MOT predictions. Allocating resources along a 1-year distribution of incident infections can prioritise prevention to the wrong subgroups because they do not account for the dynamic effects of interventions. Improved methods of epidemic appraisals are urgently needed to guide prevention programming.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.009 | 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 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".