Ensemble Machine Learning for COVID-19 Forecasting: Enhancing Resource Planning and Pandemic Response in Oman
Bibliographic record
Abstract
This present study proposes a machine-learning approach for predicting COVID-19 infection and death rates to support government resource planning in Oman. It compares three algorithms, namely Decision Tree, Random Forest, and Gradient Boost, for the best model that provides an accurate prediction of COVID-19. The Decision Tree model overfitted and gave accuracy of 99.41% in training and 53.39% in testing. On the other hand, the Random Forest model generalized better with 94.66% training accuracy versus 61.32% testing accuracy. The Gradient Boost model achieved 92.96% training accuracy and 59.44% testing accuracy but needs further tuning. A correlation analysis between the COVID-19 metrics has been presented. From the given heat map, daily new cases versus active cases represent a strong positive relation: increased active cases due to increased daily new cases. Overall case and death show a negative relation-an indication of reduced mortality rate. Detailed validation shall establish the fact that improved health services and vaccination campaigns were working in proper direction. Random Forest model was more accurate and generalized for the COVID-19 trend than the other models compared. The results from the Gradient Boost model were similar, but its performance needs further optimization. The overall findings from the study are vital in informing better public health policy improvements and effective resource management of the pandemic. This research thus contributes to the development of an efficient predictive tool to manage COVID-19 in Oman, using state-of-the-art machine learning techniques.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".