Accurate Estimation of COVID-19 Active Cases Using Bézier Curve-Based Mathematical Modeling
Bibliographic record
Abstract
COVID-19 has reminded humanity of the devastating reality of a pandemic after many years. This global crisis fundamentally altered daily life and exposed significant vulnerabilities in public health systems worldwide. This work proposes a geometric curve-based prototype to support the fight against current and future pandemics. The study models a near-perfect estimation of the active case number with the help of the Bézier curve. The Bézier prototype consists of a C0 class, piecewise continuous, and segmented structure. The noiseless and cost-free model estimates the number of active cases in Germany, Canada, and Israel with minimum error. It compares the results obtained with those of cases in China. The absolute average error of the model is reduced to 0.087%. As a result, the consistent and cost-effective model can increase the likelihood of making rapid and accurate decisions against epidemics, produce well-organized projections for the future, and improve the effectiveness of measures to be taken.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".