Hybrid Feature Engineering and Tree-Based Ensembles for Predicting Epidemic Outbreaks
Bibliographic record
Abstract
Machine learning is gaining significant prominence as a practical approach for predicting disease outbreaks and trends. Regression-based models have drawn considerable attention in the time-series disease forecasting domain. Earlier, disease prediction relied heavily on handcrafted statistical models like ARIMA and SEIR. However, the evolving nature of COVID-19 data has made machine learning-based forecasting an active area of research. Regression models like e Extreme Gradient Boosting XGBoost and Random Forest have recently shown promising results when trained on temporal clinical and demographic data. These models analyze lagged case data and mobility trends to discover low-level patterns not captured by classical models, enabling the system to anticipate infection surges more efficiently. Efficient feature engineering and model optimization are vital to achieving this. The architecture proposed in this paper integrates time-lag and rolling average features with a tuned regression pipeline for end-to-end COVID-19 prediction. The proposed approach converges quickly and outperforms conventional regressors in both accuracy and error metrics. This research work has been tested on public COVID-19 datasets and improved R-squared score by 15% and reduced RMSE by 28%.
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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) | 0.001 | 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 | 0.001 |
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".