Dynamics of the HIV Epidemics among Male Injecting Drug Users Using Agent-Based Modeling
Bibliographic record
Abstract
Background: Although Injecting Drug Users (IDUs) carry a disproportionate burden of HIV, little is known about the dynamics of the HIV epidemics among IDUs. Objective: This study aimed to characterize the dynamics of the HIV epidemic among IDUs and the effects of alternative HIV prevention intervention strategies using Agent-Based Modeling (ABM). Methods: ABM was constructed using key behavioral risks. The HIV/STI Surveillance study was utilized to create datasets for simulation. Different intervention scenarios were simulated and compared. Results: Lowering needle sharing level among IDUs resulted in the largest reductions in both HIV prevalence and the cumulative number of HIV infections over time in all simulated populations. The majority of the reductions occurred when needle sharing declined from the baseline level to 40% and 30%, respectively. Conclusion: ABM may well complement traditional epidemiological regression-based analysis in providing important insights into the complex dynamics of the HIV epidemics among IDUs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".