Dynamics of HSV-2 infection with a therapeutic vaccine
Bibliographic record
Abstract
Herpes-Simplex Virus type 2 (HSV-2) is a lifelong infection, which has infected over 400 million individuals aged 15-49 years, worldwide. While the disease can be treated with episodic and suppressive antiviral drugs to reduce the rate of recurrence (i.e., symptomatic disease) and viral shedding, none of the currently available therapies can clear the virus from the body of an infected person. A number of therapeutic vaccine platforms are currently in development in order to achieve similar effects to treatment. Due to the inadequate data from clinical trials of therapeutic vaccines, modeling efforts to quantify the impact of vaccination have been limited. In this study, we propose a compartmental deterministic model for the dynamics of HSV-2 to evaluate the effect of a potential vaccine candidate with the inclusion of a booster dose. Despite its simplicity that may not address the complexity of HSV-2 disease, the model shows that targeting symptomatic infection for vaccination is the most effective strategy in the long-term. This conclusion is based on the assumption of an optimal vaccine efficacy, conferring immunity levels that prevent viral shedding and recurrence transiently. Our model provides a framework for developing a computational system to include more heterogeneous characteristics of the disease and individuals, and investigate effectiveness and cost-effectiveness of vaccination scenarios when clinical data 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".