A Deterministic Mathematical Dynamic System Based on the PSITPS Model for Modeling the COVID-19 Epidemic
Bibliographic record
Abstract
To illustrate the dynamics of the COVID-19, we have introduced a mathematical model called PSITPS and shown the effect of protection (vaccination) after treatment.The proposed model solution's positivity, boundedness, existence, and uniqueness are analyzed.The model's possible equilibrium points were also identified, and the Next-Generation Matrix was employed to calculate the Basic Reproduction Number ℛ 0 .This study dealt with the stability of equilibrium points at the local and global levels under specific conditions.The Disease-Free Equilibrium point is locally asymptotically stable when ℛ 0 < 1; otherwise, it's unstable.By creating the Lyapunov function, we showed that the endemic equilibrium points are globally stable.In this work, we conducted numerical simulations of the model using true data from the COVID-19 epidemic in Najaf.It is a city where religious events abound with large gatherings, which lead to violation of health instructions to avoid infection with COVID-19.The simulation showed that protection (vaccination) after treating the infected had a substantial effect on mitigating the spread of COVID-19.The paper highlights the role of vaccination as a protective measure in effectively controlling the transmission of COVID-19 and mitigating the incidence of illness within the community.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".