Comparative study of machine learning methods for influenza outbreak forecasting
Bibliographic record
Abstract
The contagious disease influenza that is prevalent before and during the Covid-19 era poses a substantial effect on the global health care system. Traditional computational epidemiology can simulate potential disease progress but is inefficient in gathering sophisticated and real-time data sets. Social media, the world’s most widespread data generator, provides an access to more updated epidemic surveillance. Confronted with the outburst of the Covid-19 pandemic and concurrent influenza, machine learning models are the more efficient and crucial technique in mapping the next influenza outbreak with social media data. This study utilized supervised machine learning techniques including Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) to estimate the risk of influenza outbreaks, and examined the potential of different forecasting models in predicting the outbreak. The content of the social media was modeled based on its correlations with disease outbreaks and computed through various statistical models. Comparisons of the result revealed RF as the most efficient forecasting model for influenza outbreaks during the Covid-19 pandemic and demonstrate the usefulness of this study in future disease prediction.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".