Mathematical Modeling to Assess the Impact of Covid-19 Transmission in Guyana, South America
Why this work is in the frame
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Bibliographic record
Abstract
Objective: This study aims to find the best mathematical model for modeling the Covid-19 data of Guyana. Methods: The 2-parameter, 3-parameter Weibull distribution, and the Transmuted Weibull Distribution was used to model the Covid-19 data of Guyana using cumulative deaths that occurred on a daily basis from March 12th, 2020 to November 30th, 2021. The Covid-19 data of Guyana was extracted from the ‘ourworldindata’ website. Findings: The transmuted Weibull distribution is the best model for modeling the Covid-19 data of Guyana since it had the lowest AIC value than the other models. Novelty: Several transmuted distributions were developed to model the Covid-19 data of France, the United Kingdom, and Canada. However, in this study, a different transmuted distribution was chosen to model the Covid-19 data of Guyana. Keywords: Mathematical Modeling; Covid19; Cumulative Deaths; Transmuted Weibull Distribution and Simulation Study
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it