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.
By Dahai Yu, Lin Jiang, Rongchao Xu, Guang Wang
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.
By Humaira Anzum, Md Ishtyaq Mahmud, Jagan Mohan Reddy Dwarampudi, Tania Banerjee
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.
By Weixiong Hua, Fan Bu
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.
By William Xing, Stephanie Yang, Aarush Bandemegal, Anushree Misra, Ananya Kalapatapu, Brennan Lagasse, Kevin Zhu
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.
By Minwei Zhao, Sanja Scepanovic, Stephen Law, Ivica Obadic, Cai Wu, Daniele Quercia
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...
By Shuren He, Huiyan Sang, Ligang Lu
arXiv:2607. 22875v1 Announce Type: cross Abstract: Accurate estimation of soil microplastics and organic matter is essential to assess ecosystem health and support sustainable land use.
By Anik Dev Nath, Md Al Amin, Bikash Kumar Paul
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.
By Rajdeep Pathak, Tanujit Chakraborty
arXiv:2608. 19672v1 Announce Type: new Abstract: Natural hazard susceptibility mapping is often constrained by limited labeled data, reducing the generalizability of conventional machine learning and limiting the applicability of complex deep learning models.
By Mohammad H. Vahidnia, Ali Pourkarimi
arXiv:2606. 08046v1 Announce Type: new Abstract: We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data.
By Dimitrios Michail, Eleni Saka, Ioannis Giannopoulos, Ioannis Papoutsis
arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.
By Daniele Mos, Felipe Drummond, Anton Bossenbroek, Soufiane el Khinifri
arXiv:2606. 14313v1 Announce Type: cross Abstract: Real-world spatio-temporal forecasting must handle irregular time points, spatially sparse observations, and the need for uncertainty quantification.
By Jaeyeong Lee, Heeyoung Kim