A Neural Network-Based Model for Real-Time Prediction of Infectious Disease Dynamics*
Bibliographic record
Abstract
Accurately predicting infectious disease dynamics is crucial for effective public health management and decision-making. Traditional epidemiological models, such as the Susceptible-Infected-Recovered (SIR) framework, often struggle to capture the complex and evolving nature of outbreaks due to their reliance on predefined system parameters. This paper presents a three-layer neural network (NN) model for real-time prediction of infectious disease dynamics without requiring prior knowledge of the system. Unlike conventional methods, the proposed NN incorporates an integral component into its update rules, allowing prediction accuracy to improve over time by accounting for both current and historical errors. The model is validated using both simulated SIR data and real-world infectious disease datasets, consistently demonstrating superior predictive accuracy. It outperforms baseline methods, including traditional and autoregressive networks, in both accuracy and adaptability, highlighting its potential for real-time forecasting and public health planning. The computational efficiency of the method makes it suitable for real-time applications in evolving disease environments.
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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 | 0.000 |
| 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) | 0.002 | 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".