Age-Structured SILV Epidemic Model on HPV and Cellular Dynamics with Implicit Impact of Vaccination
Bibliographic record
Abstract
The implicit impact of vaccination on susceptible cells (epithelial layer) is studied on the basis of stability analysis of age-structured epidemic model of susceptible cells, infected cells and cells of lesion tissue (dysplasia and cancer), human papillomavirus (HPV). The efficacy of the vaccine indirectly influences the coefficients of the system, thereby determining the types of dynamical regime of the HPV and cellular population. The model possesses unique disease-free (DFE) and unique endemic equilibria (EE) (Theorem 1). The asymptotically stable DFE is associated with the resilience of epithelial layer of vaccinated organism to HPV infection while the asymptotically stable EE is associated with the resilience of the lesion tissue of epithelial layer to treatment. The analysis of the model reveals independent factors affecting the stability/instability of DFE and EE (Theorems 2, 3): (i) cell death rate and proliferation rate, (ii) HPV infection rate, budding number of HPV virions, apoptosis rate of infected cells and HPV death rate (parameters of the implicit influence of vaccine efficacy), and (iii) DFE value of epithelial tissue size (environmental capacity of HPV depending on the initial size of the epithelial layer). Thus, HPV vaccine efficacy should be sufficiently high to guarantee the asymptotic stability of DFE with the epithelial tissue of large possible size, which can be taken into account when studying the efficacy of new vaccines in control groups in clinical trials.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".