Modelling the interactions between herpes simplex virus type 2 and HIV: implications for the HIV epidemic in southern India
Bibliographic record
Abstract
BACKGROUND: The role of herpes simplex virus type 2 (HSV-2) in the HIV epidemic and the potential impact of HSV-2 suppressive therapy have previously been explored only within the context of sub-Saharan Africa. In this analysis, modelling is used to estimate the contribution of HSV-2 to HIV transmission from clients to female sex workers (FSW) in a southern Indian setting and the maximum potential impact of 'perfect' HSV-2 suppressive therapy on HIV incidence. METHODS: A dynamic HSV-2/HIV model was developed, parameterised and fitted to Mysore data. The model estimated the attributable fractions of HIV infections due to HSV-2. Multivariate sensitivity analyses and regression analyses were conducted. RESULTS: The model suggests that 36% (95% CI 22% to 62%) of FSW HIV infections were due to HSV-2, mostly through HSV-2 asymptomatic shedding. Even if HSV-2 suppressive therapy could eliminate the effect of HSV-2 on HIV infectivity among all co-infected clients, only 15% (95% CI 3% to 41%) of HIV infections among FSW would have been averted. 36% (95% CI 18% to 61%) of HIV infections among HSV-2-infected FSW could have been averted if suppressive therapy reduced their risk of HIV acquisition to that of HSV-2-uninfected FSW. CONCLUSIONS: HSV-2 contributes substantially to HIV in this southern Indian context. However, even in the best case scenario, HSV-2 suppressive therapy is unlikely to reduce HIV transmission or acquisition by more than 50% (as aimed for in recent trials), because of the limited strength of the interaction effect between HSV-2 and HIV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".