A deterministic compartmental model for investigating the impact of escapees on the transmission dynamics of COVID-19
Bibliographic record
Abstract
The recent outbreak of the novel coronavirus (COVID-19) pandemic has devastated many parts of the globe. Non-pharmaceutical interventions are the widely available measures to combat and control the COVID-19 pandemic. There is great concern over the rampant unaccounted cases of individuals skipping the border during this critical period in time. We develop a deterministic compartmental model to investigate the impact of escapees (individuals who evade mandatory quarantine) on the transmission dynamics of COVID-19. A suitable Lyapunov function has shown that the disease-free equilibrium is globally asymptotically stable, provided R0<1. We performed a global sensitivity analysis using the Latin-hyper cube sampling method and partial rank correlation coefficients to determine the most influential model parameters on the short and long-term dynamics of the pandemic to minimize uncertainties associated with our variables and parameters. Results confirm a positive correlation between the number of escapees and the reported COVID-19 cases. It is shown that escapees are primarily responsible for the rapid increase in local transmissions. Also, the results from sensitivity analysis show that an increase in governmental role actions and a reduction in the illegal immigration rate will help to control and contain the disease spread.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".