UK and other SARS-CoV-2-Covariants - Simulation Modeling 70% Increase
Bibliographic record
Abstract
ABSTRACT CovidSIMVL, an agent-based contagion-based viral transmission simulation tool, was employed to simulate the effects of viral agents of differing levels of infectivity. The constructs “Velocity” and “Increase in Velocity” were operationalized in terms of rates of transmission events over successive iterations (generations) in a set of CovidSIMVL trials. Treating 40-70% increase in velocity as a target, based on reports in the literature for the UK variant ( VUI 202012/01) , the series of trials reported in the paper demonstrate the calibration of CovidSIMVL parameters to produce increases in transmission rates of 40-70% above a baseline value. A series of follow-up studies is proposed to evaluate three different possible explanations for reported increases in SARS-Cov2-2 infections that are being attributed to spread of the UK and other variants: (a) simulations where the inherent characteristics of the virus (infectivity) are varied (genomic studies); (b) simulations where the behaviour of agents is varied (e.g., movement within and between spaces) while inherent characteristics of the virus are held constant (behavioural studies); and (c) simulations where both inherent properties of the virus and the behaviour of agents are varying to “tease out” the interaction between biologically-based contributions to increased case counts, and contextual/behavioural contributions (epigenetic studies).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".