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.
The paper introduces Empirical Research Assistance (ERA), an autonomous system that employs Large Language Model‑guided tree search to create, evaluate, and refine executable forecasting software for infectious diseases. In a real‑time evaluation during the 2025‑26 U.S. respiratory season, ERA produced diverse models for influenza, COVID‑19, and RSV, and an ensemble of these models outperformed the CDC’s gold‑standard forecast ensembles across all three pathogens. ERA also demonstrated robustness in data‑scarce RSV scenarios and showed that higher‑tier language models and specific optimization metrics can improve forecast accuracy.
arXiv:2608. 06671v1 Announce Type: new Abstract: Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks.
arXiv:2608.20406v1 Announce Type: new Abstract: Public health forecasts must respond to abrupt changes in surveillance data without over-extrapolating noise, reporting artifacts, or temporary trends....
arXiv:2601. 20771v2 Announce Type: replace-cross Abstract: Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention.
arXiv:2511. 23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports.
arXiv:2603.25495v2 Announce Type: replace-cross Abstract: Accurate short-term air-quality forecasting is essential for public health protection and urban management, yet many recent forecasting frame...
arXiv:2606. 00834v1 Announce Type: cross Abstract: Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary transmission dynamics reduce the reliability of conventional models.
PGP-Clinical-TimeKAN is a trajectory-first framework for joint probabilistic forecasting of multivariate physiological data, combining missingness-aware temporal encoders, a soft organ-system prior, patient-specific relations, nonlinear Kolmogorov‑Arnold messages, and a low‑rank multivariate Student‑t head. Evaluated on a MIMIC‑IV cohort of 6,882 patients, it achieves the second‑lowest normalized MAE and the lowest RMSE among 13 models, while providing calibrated probabilistic forecasts with empirical coverage at 50%, 80%, and 95% intervals. Ablation studies show that relational structure is critical for performance, and increasing covariance rank improves likelihood but not point accuracy. whyItMatters":"The model demonstrates that joint trajectory forecasting can yield highly accurate, calibrated predictions of physiological trajectories, offering a potentially inspectable intermediate task for clinical deterioration prediction."
arXiv:2607. 27106v1 Announce Type: new Abstract: Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits.
arXiv:2610.28563v1 Announce Type: new Abstract: Online search activity is widely used as a digital trace of information seeking during epidemics, but its predictive value is often reduced to correlat...
The paper introduces a compact patient world model that forecasts digital health campaign outcomes by maintaining a latent state per patient and learning exposure‑conditioned dynamics. Evaluated on a large US campaign dataset, the model predicts new‑to‑brand prescription volume with low relative error (2.9% at week‑4 cutoff) compared to much higher errors from baseline classifiers. The study also shows that dense next‑exposure supervision is crucial for accurate forecasts when conversions are rare and highlights limitations in interpreting exposure‑conditioned rollouts causally.