Stability Analysis of Spread of Infectious Diseases COVID-19 Using SEIAR-V1V2Q Model for Asymptomatic Condition with Runge-Kutta Order 4
Bibliographic record
Abstract
The spread of infectious diseases in Indonesia has become a significant concern in health.COVID-19 contagious disease has difficulties in infection because some individuals are infected asymptomatically.Infectious diseases are modeled with a SEIR model modification with vaccinations 1 and 2, and Quarantine will produce a new approach by considering the variables and parameters of prevention so that it can suppress the rate of spread of the infectious disease COVID-19.The study will simulate a numerical analysis of the transmission model of contagious diseases solved using the Runge-Kutta order 4. The results achieved a new model with the SEIAR-V1V2Q modification, this model can predict the acceleration of the spread of the COVID-19 transmitted disease by considering asymptomatic conditions.Based on the research, modifying the SEIR model with 2-stage vaccination and quarantine measures can reduce the percentage of infection cases of susceptible individuals, especially cases of asymptomatic infection, which are cases of infected individuals without showing symptoms.Discipline in accelerating 2-stage vaccination will increase the formation of individual body immunity to strengthen unique antibodies to minimize infection with the COVID-19 virus.It can be a reference in similar cases requiring vaccination and Quarantine of infected individuals.
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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 | 0.000 |
| 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) | 0.003 | 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".