Forecasting infectious disease outbreak risks from vaccine sentiments on social media: A data-driven dynamical systems approach
Bibliographic record
Abstract
Early warning signals are vital in predicting critical transitions in complex dynamical systems. For behavioral epidemiology systems in particular, this includes shifts in vaccine sentiments that may precede disease outbreaks. Conventional statistical indicators, such as variance and lag-1 autocorrelation, often struggle in noisy environments and may fail in real-world scenarios. In this study, we leveraged universal signals of critical slowing down to train deep learning classifiers, specifically using long short-term memory (LSTM) and residual neural network (ResNet) architectures, for detecting early warning signals in disease-related social media time series. These classifiers were trained on simulated data from a stochastic coupled behavior-disease model with additive Lévy noise, a non-Gaussian noise that better reflects the heavy-tailed nature of real-world fluctuations. Our results show that these classifiers consistently outperform conventional indicators in both sensitivity and specificity on theoretical data while delivering quantitatively clear results that are easier to interpret on empirical data. Integrating deep learning with real-time social media monitoring offers a powerful tool for preventing disease outbreaks through proactive public health interventions.
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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 | 0.005 |
| 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".