An SVEICRD model for assessing the impact of the lock-down intervention and vaccination strategies on the spread of COVID-19
Bibliographic record
Abstract
In this research, we aim to forecast the trajectory of the COVID-19 pandemic in terms of the number of exposed, infected, vaccinated, hospitalized, recovered, and dead people, and observe the effects of different vaccination strategies on the spread of the COVID-19. We simulate the ongoing trajectory of the outbreak in three countries, namely, Canada, the UK, and Israel using the susceptible - vaccinated - exposed - infected - critical - recovered - dead (SVEICRD) model. We consider two vaccination strategies and investigate their effects on the number of exposed and death cases. We perform an extensive numerical study to assess the implications of different strategies and spread scenarios. Our findings confirm that the fourth wave has begun in all three countries, and already reached its peak. We observe that starting second dose vaccination as early as possible is the most effective in mitigating the spread of COVID-19, although it does require more vaccination supply than the alternative strategies. Our results show that the SVEICRD model successfully forecasts the changing number of people in each compartment and the vaccination strategy significantly impacts the trajectory of the outbreak.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".