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HealthMamba is an uncertainty‑aware spatiotemporal graph state space model designed to predict healthcare facility visits more accurately and reliably. It integrates a Unified Spatiotemporal Context Encoder, a novel GraphMamba module for hierarchical modeling, and a comprehensive uncertainty quantification component. Evaluated on large‑scale datasets from California, New York, Texas, and Florida, it improves prediction accuracy by about 6.0% and uncertainty quantification by 3.5% over state‑of‑the‑art baselines.
arXiv:2607. 13508v1 Announce Type: new Abstract: Quantifying directional influence between node populations is a fundamental problem in graph-based modeling, particularly in spatial biological systems where cell-cell interactions shape functional outcomes.
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:2607. 19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells.
arXiv:2504. 04739v3 Announce Type: replace Abstract: Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy.
arXiv:2609.36046v1 Announce Type: cross Abstract: In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across...