Strengthening deep learning model in forecasting of COVID-19 epidemics in selected countries
Bibliographic record
Abstract
Accurate forecasting of COVID-19 case dynamics is essential for facilitating robust public health strategies and interventions is critical for effective public health planning and response. This study introduced novel deep learning models that integrate autocorrelation (ACF) and partial autocorrelation (PACF) into LSTM and GRU networks to improve forecasts of cumulative confirmed, recovered, and death cases. Six variants—ACF/PACF-enhanced LSTM and GRU models—were compared with standard LSTM, GRU, and ARIMA across eleven countries during non-vaccination, vaccination, and entire study period. The results show that integrating ACF and/or PACF features notably enhances forecasting accuracy. ACF-PACF-GRU outperformed baseline models in predicting confirmed and death cases during the vaccination period, especially in countries like South Africa, Canada, Denmark, and Brazil. While performance varied by country and epidemic dynamics, standard LSTM or GRU models still performed well. This research highlights the importance of incorporating temporal dependencies into deep learning models and offers valuable insights for public health authorities seeking to deploy data-driven tools for epidemic preparedness and response.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".