arXiv:2606. 30842v1 Announce Type: new Abstract: Outbreak transmission reconstruction treats epidemiological timing and transmission labels as deterministic ground truth; neither has been systematically evaluated.
arXiv:2608. 02633v1 Announce Type: new Abstract: Regional surveillance data reflect local transmission, reporting, seeding, and external infection pressure, which are difficult to identify separately.
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: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. 19262v1 Announce Type: new Abstract: As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis.
arXiv:2606. 05168v1 Announce Type: cross Abstract: Training on synthetic data causes model collapse, but existing analyses treat this as single-chain degradation.
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
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 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. 14028v1 Announce Type: cross Abstract: In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation even when unlabeled target inputs are available.
arXiv:2607. 16234v1 Announce Type: cross Abstract: Hantavirus genomic surveillance is limited by the distribution of sequence data, non-IID source heterogeneity, and constrained expert-review capacity.
arXiv:2606. 27286v1 Announce Type: new Abstract: Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making.
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.