A Review of COVID-19 Mathematical Models and an Implementation of Vaccination Policies
Bibliographic record
Abstract
Cellular automata (CA) models have been used to simulate the behaviour of infectious diseases and can offer valuable information concerning the spread of infection and population susceptibility to sickness. Furthermore, CA models can be used to evaluate various response strategies, such as herd immunization and vaccination. By assessing CA approaches for COVID-19, we identified a lack of an age-based symptom severity score and death probability (1-3). We used a Susceptible Infected Removed (SIR) model and distribution of population age groups from the Canadian government to determine the effect of vaccinating older (60+ years old) versus a younger population (30-59 years old). Our findings show a significant decrease in the total number of deaths and peak number of infections when the older population was vaccinated. This is a result of the higher probabilities of death and severe symptoms in older age groups. While this simulation is based on a small scale, the findings provide evidence to prioritize vaccination of the elderly.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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".