On Statistical Analysis of Forecasting COVID-19 for the Upcoming Months in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
This paper presents a statistical analysis using fitted prediction models that revealed a high exponential growth in the number of confirmed cases, deaths, and treated case processes based on our model predictions and the results of experimental COVID-19 predictions. The studies aimed to build inductive statistical models using the automatic integrated mean regression model methodology, and its preferred method for tracking data that represent the spread of the epidemic and then effectively predicting its numbers over the next six months, in addition to the number of deaths and cases that responded to recovery treatment using ARIMA. Moreover, the number of infected cases per day is expected to stabilize less than 500, daily deaths are less than 15, and this situation will continue until the largest number of people are vaccinated in order to obtain herd immunity, and control the causes of the spread of the epidemic such as human gatherings and friction. Among individuals, in addition to obtaining the appropriate vaccine in the future, especially since the Kingdom of Saudi Arabia is waiting for this year's pilgrims from inside and outside the Kingdom, the results of this work will be useful for practitioners in various fields of theoretical and applied sciences.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".