SVLIAR age-of-infection and -immunity structured epidemic model of COVID-19 dynamics
Bibliographic record
Abstract
Stability analysis of nonlinear age-of-infection and -immunity structured SVLIAR-type model of susceptible, vaccinated, latent, COVID-19 infected, asymptomatic and recovered sub-classes of population dynamics is carried out in this paper. The SVLIAR model uses five age variables - age of vaccine immunity of vaccinated individuals, age of virus infection in organism during incubation period of latent individuals, “age” of infectious disease treatment of infected individuals, age of asymptomatic infectious dis-ease of asymptomatic individuals, “age” of immunity of organism after recovering of recovered individuals. Individuals can move from one subclass to another when these age variables take some fixed values, that is the processes in sub-classes are adjusted and synchronized by age variables. The conditions for the existence of disease-free and unique endemic equilibria and their local asymptotic stability were obtained. The local asymptotic stability/instability of endemic equilibrium of SVLIAR model is defined by criterion, which relates the demographic characteristics of population, infection disease characteristics (disease-induced death rate, death rate induced by the complications after disease), characteristics of vaccination (fraction of fully vaccinated susceptibles per unit of time, vaccination efficacy) and characteristics of age variables (their maximum values) of sub-classes. These theoretical results help understand better the conditions of transmission dynamics of the COVID-19 induced disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".