A Hybrid Time Series–Regression Model for Tuberculosis Forecasting in Resource-Limited Settings
Bibliographic record
Abstract
Tuberculosis (TB) is still a serious public health issue in Sudan, especially in Gedaref State, because of limited medical facilities and inadequate disease reporting. This experiment develops a forecasting model by employing Seasonal Trend decomposition using LOESS (STL) and linear regression in combination, relying on the weekly tests to improve TB prediction. The model improves the accuracy of its forecasts by combining time series information with the details of the daily operations of the health system. Weekly data from Gedaref showed that the STL + regression approach performed better than ARIMA, reducing the root mean squared error (RMSE) from 2986.85 to 540.95, an improvement of about 81.9%. The model also remained flexible to fluctuations in testing volume. The findings illustrated that hybrid statistical methods have been proved to be reliable and practical in forecasting TB cases in situations where limited resources exist, providing a strong base for overseeing TB and other communicable diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".