Modelling and Analysis of Vaccination Effects on Hand, Foot, and Mouth Disease Transmission Dynamics
Bibliographic record
Abstract
In this study, the transmission dynamics of hand, foot, and mouth disease (HFMD), incorporating vaccination, were comprehensively assessed.A Susceptible-Vaccinated-Exposed-Infectious-Recovered (SVEIR) model was formulated and its stability was evaluated in relation to disease-free and endemic equilibrium points.The fundamental reproduction number, R0, was derived utilizing the Next-Generation Matrix method.This work demonstrates the local and global asymptotic stability of both disease-free and endemic equilibria under defined conditions.The local stability of the disease-free equilibrium set was ascertained via the Jacobian matrix method, contingent upon certain prerequisites.Conversely, the stability of the endemic equilibrium set was affirmed using the Routh-Hurwitz criteria.In the context of global stability, a Lyapunov function was employed to establish the disease-free equilibrium case, demonstrating that the equilibrium E0 is globally asymptotically stable within region Ω.Stability of the endemic equilibrium set for the susceptible and infected compartments was exhibited using Dulac's criteria.Additionally, a sensitivity analysis was performed, revealing a significant correlation of the basic reproduction number to specific parameters, namely A, β1, β2, β3, β4, and ρ.This analysis indicates that these aforementioned parameters have a substantial influence on HFMD propagation.The analytical findings were corroborated through numerical simulations which further reinforced the validity of the model.This work presents a profound exploration of HFMD transmission dynamics, offering valuable insights for the development of efficacious control strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".