A Transmission Dynamics Model of COVID-19 With Consideration of the Vulnerability of a Population
Bibliographic record
Abstract
A well documented characteristic of COVID-19 is that whereas certain infected individuals recover without ever showing symptoms, others regarded as vulnerable, usually age with comorbidities tend to succumb to more or less severe symptoms. To address pertinent issues, we formulate an $SEI_{A}I_{S}RS$ Transmission Dynamics model of COVID-19 where $I_{A}$ and $I_{S}$ respectively represent asymptomatic and symptomatic classes thus allowing the inclusion of parameters which are vulnerability sensitive. We define a vulnerability factor, $\phi$ and show that the model is globally asymptotically stable at the disease-free equilibrium when $\mathcal{R}_{0}<1$ and $\phi$ is appropriately bounded above. We also show that the model is globally asymptotically stable at the endemic equilibrium when $\mathcal{R}_{0}>1$ and $\phi$ is appropriately bounded below. Finally, we employ numerical analysis using Ghana data, to further illustrate the effect of vulnerability related parameter values on the trajectories of key variables of the model. We thereby demonstrated that if a dominantly young population is of sufficiently low vulnerability then $\mathcal{R}_{0}<1$, and the Transmission Dynamics exhibits global asymptotic stability at the disease-free equilibrium.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".