MATHEMATICAL MODELING OF COVID-19 PHENOMENON; THE CASES: GERMANY, ISRAEL AND CANADA
Bibliographic record
Abstract
Epidemic diseases are described as a pandemic that affects the enormous majority of the world, spreads rapidly among people, and causes deaths. Negative effects, the number of casualties, rates of spread, and the duration of the commencement and the end of such outbreaks differ from each other and depend on the regions of effect, the processes of the vaccination studies, and cure. Recently a virus has been discovered which has caused a pandemic that has threatened all the world: COVID-19. It is a kind of coronavirus that emerged originally among chickens in 1960s. This essay introduces COVID-19 cases using a mathematical method. It carefully examines data from worldometers, makes models, and estimations. The data discussed under titles such as Total Case, Outside China, Active Case, Total Cured, Critical Case, Germany, Israel, and Canada are analyzed without isolating their context. While the maximum-minimum ranges and standard deviations of the variables are displayed by a descriptive analysis, binary relations are observed by the correlation matrix. While the homogeneous distribution of the data is determined by factor analysis, hierarchical groups are expressed by cluster analysis. Future estimations are presented by data models presented to the reader with nine different variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".