arXiv:2602. 06323v2 Announce Type: replace Abstract: Epidemiological forecasting from surveillance data is a hard problem and hybridizing mechanistic compartmental models with neural models is a natural direction.
arXiv:2606. 19560v1 Announce Type: new Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need.
arXiv:2605. 26704v2 Announce Type: replace-cross Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points.
arXiv:2607. 26854v1 Announce Type: new Abstract: Accurate multi-week dengue forecasting supports timely vector-control interventions, outbreak preparedness, and healthcare resource allocation.
The paper introduces Influpaint, a denoising diffusion probabilistic model adapted for forecasting influenza incidence. By representing influenza seasons as spatiotemporal images and training on a hybrid dataset of surveillance and simulated trajectories, the model learns a rich distribution of disease dynamics and performs forecasting as a conditional generation task. In retrospective and real‑time evaluations, Influpaint produces realistic, diverse epidemic trajectories and achieves forecast accuracy competitive with leading ensemble methods, especially when trained with 30% surveillance and 70% simulated data.
arXiv:2606. 05692v1 Announce Type: new Abstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes.
arXiv:2603. 07108v2 Announce Type: replace-cross Abstract: Accurate and reliable forecasting of epidemic incidences is critical for public health preparedness, yet it remains a challenging task due to complex nonlinear temporal dependencies and heterogeneous spatial interactions.
The paper introduces TERN, a forecasting model that uses a delta‑rule fast‑weight memory with channel‑wise decay and learned erasure gates, combined with a seasonal reference and online adaptation. It addresses challenges in influenza forecasting such as limited seasonal data, shifting wave patterns, and misleading information after peaks. On three Cola‑GNN influenza benchmarks, TERN outperformed existing epidemic graph models and general forecasters, matching or exceeding seasonal references and demonstrating the value of its memory component.
arXiv:2606. 05513v1 Announce Type: new Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time.
Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics.
Weekly influenza surveillance counts guide vaccine distribution and public-health alerts, yet they are hard to forecast. Each region offers only a few seasons, waves shift in timing and height every y...
arXiv:2606. 05168v1 Announce Type: cross Abstract: Training on synthetic data causes model collapse, but existing analyses treat this as single-chain degradation.
arXiv:2608.23373v1 Announce Type: new Abstract: Forecasting long-range influenza-like illness (ILI) matters for public health readiness. Publicly available surveillance datasets typically pair numeri...