What Is the Burden of Heterosexually Acquired HIV Due to HSV-2? Global and Regional Model-Based Estimates of the Proportion and Number of HIV Infections Attributable to HSV-2 Infection
Bibliographic record
Abstract
BACKGROUND: Biological and epidemiological evidence suggest that herpes simplex virus type 2 (HSV-2) elevates HIV acquisition and transmission risks. We improved previous estimates of the contribution of HSV-2 to HIV infections by using a dynamic transmission model. SETTING: World Health Organization regions. METHODS: We developed a mathematical model of HSV-2/HIV transmission among 15- to 49-year-old heterosexual, non-drug-injecting populations, calibrated using region-specific demographic and HSV-2/HIV epidemiological data. We derived global and regional estimates of the contribution of HSV-2 to HIV infection over 10 years [the transmission population-attributable fraction (tPAF)] under 3 additive scenarios, assuming: (1) HSV-2 increases only HIV acquisition risk (conservative); (2) HSV-2 also increases HIV transmission risk (liberal); and (3) HIV or antiretroviral therapy (ART) also modifies HSV-2 transmission risk, and HSV-2 decreases ART effect on HIV transmission risk (fully liberal). RESULTS: Under the conservative scenario, the predicted tPAF was 37.3% (95% uncertainty interval: 33.4%-43.2%), and an estimated 5.6 (4.5-7.0) million incident heterosexual HIV infections were due to HSV-2 globally over 2009-2018. The contribution of HSV-2 to HIV infections was largest for the African region [tPAF = 42.6% (38.0%-51.2%)] and lowest for the European region [tPAF = 11.2% (7.9%-13.8%)]. The tPAF was higher among female sex workers, their clients, and older populations, reflecting their higher HSV-2 prevalence. The tPAF was approximately 50% and 1.3- to 2.4-fold higher for the liberal or fully liberal scenario than the conservative scenario across regions. CONCLUSION: HSV-2 may have contributed to at least 37% of incident HIV infections in the past decade worldwide, and even more in Africa, and may continue to do so despite increased ART access unless future improved HSV-2 control measures, such as vaccines, become available.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".