Clustering for Moroccan Prefecture-Provinces and World Countries Based COVID-19 Dataset
Bibliographic record
Abstract
This paper describes the clustering technique for provinces-territories in Morocco and countries of the world at risk of the COVID-19 epidemic.Based on this proposed method, we have used COVID-19 Moroccan dataset, on August 18, 2021, with the higher new death number.The COVID-19 dataset for countries is based from the Worldometer on November 25, 2021.In this study, we employed K-Means algorithm, Elbow -Silhouette Methods and statistics analysis using new 'Confirmed -Death' two-dimensional data for Moroccan prefectures -provinces and new 'Confirmed-Death-Recovered' three-dimensional data for world countries.Our results show that, the clustering method generated 3 prefectureprovincial groups for Morocco, with similar types of 'Confirmed -Death' cases, and is able to group world countries into 4 clusters, with similar types of 'Confirmed -Death -Recovered' cases.Our study can be considered as a model for all countries, for analysis of COVID-19, and help political leaders and health authorities make the right decisions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".