Innovative survival modeling in pandemics with a novel family of distributions: a comparative study of UK and Mexico pandemic data
Bibliographic record
Abstract
Statistical approaches have broad applications in almost all areas of life, especially education, hydrology, reliability, administration, and healthcare. Statistical investigation and data forecasting are critical components of medical decision-making and outcome improvement. This article introduces an innovative generator based on the inverted trigonometric function, especially the Arccosecant Φ family of distributions, with the Burr distribution as the foundation model. This technique establishes the distributional features and adaptability of the Arccosecant-Burr distribution (for reference ACBD). The practicality of the model is demonstrated by comparing it to two datasets from the survival analysis. The first set of information indicates the fatality rate among individuals in Mexico who contracted coronavirus disease 2019 (COVID-19). The second data set shows the death rate of COVID-19 sufferers in the United Kingdom. Several estimation approaches are utilized to determine the unknown parameters of the ACBD distribution. The evaluation of these data sets reveals that the generator outperformed other models, indicating greater effectiveness.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".