On the Coronavirus Disease Death Rate Modeling Utilizing Generalized Exponential Kumaraswamy
Bibliographic record
Abstract
This paper aims to model COVID-19 in different countries, including Iran, Canada, Italy, and Mexico. A novel five-parameter lifetime distribution termed the Odd generalized exponential Kumaraswamy-inverse exponential distribution (OEKIE) is presented by combining the Kumaraswamy-inverse exponential distribution with the odd generalized exponential generator. The theoretical features of the new distribution, as well as its reliability functions, moments, and order statistics are investigated. The novel distribution has numerous advantages, since its density has a variety of symmetric and asymmetric forms. Furthermore, The graphs of the hazard rate function exhibit various asymmetrical shapes such as decreasing, increasing, upside-down bathtub shapes, and inverted J-shapes making OEKIE suitable for modeling hazards behaviors more likely to be observed in practical settings like human mortality, and biological applications. The OEKIE’s parameters are estimated using the maximum likelihood approach. The effectiveness of OEKIE is demonstrated through both numerical study and applications to four COVID-19 mortality rate data sets. The OEKIE provides best fits to COVID-19 data compared to other extended forms of the Kumaraswamy and inverse exponential distributions which may attract wider applications in different fields.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".