This study analyzes 2,850,575 ambulance attendances in Victoria, Australia, to quantify ambulance ramping—the delay between hospital arrival and patient handover—across 59 hospitals and 79 local government areas from January 2020 to March 2024. It finds that cumulative ambulance hours lost (AHL) totalled 1,491,127 hours, with ten hospitals accounting for 57.8 % of lost hours. Ramping duration increases with the number of ambulances arriving at the same hospital in the preceding hour and shows a moderate correlation with hourly demand two to four hours later.
By Ayesha Tanveer, Khandakar Ahmed, Assefa Teshome, Oyetunde Gbadeyan, Ziad Nehme
arXiv:2607. 04416v1 Announce Type: new Abstract: Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have paradoxically declined, pointing to the confounding role of healthcare access.
By Rahim Iqbal, Asfi Ahamed, Izzath Nisfer, Shazan Shaheed, Muhammadu Ilham, Nathali Athukorala, Madara Mendis, Nisansa de Silva, Sandareka Wickramanayake
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.
By Filipa Lino, B\'arbara Tavares, Carlos Santiago, Cl\'audia Soares, Manuel Marques
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these...
arXiv:2608. 08321v1 Announce Type: new Abstract: Floods and landslides often co-occur, but their relationships with environmental controls vary spatially.
By Aswathi Mundayatt, Siddharth Anil, Hitanshu Seth, Jaya Sreevalsan-Nair
The paper presents a transfer‑learning approach that adapts a multimodal spatiotemporal vision transformer, originally trained on Demographic and Health Survey data, to estimate socioeconomic conditions in forced‑displacement settings. Using satellite‑derived geospatial covariates, the adapted model explains up to 66% of variation in socioeconomic outcomes in camp‑intersecting grids and 41% in non‑camp areas, achieving mean absolute errors of 4.37 and 5.41 index points respectively. This framework supplements periodic household surveys by providing regularly updated, spatially granular socioeconomic estimates that bridge data gaps between survey rounds.
By Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima, Hai-Anh H. Dang, Patrick Michael Brock
arXiv:2601. 14609v2 Announce Type: replace-cross Abstract: Multi-site collaboration can power survival models that no single hospital could fit alone, but privacy rules and protected computing environments block patient-level data sharing and the persistent server connections required by iterative federated methods.
By Ziwen Wang, Siqi Li, Marcus Eng Hock Ong, Nan Liu
arXiv:2502. 06866v3 Announce Type: replace-cross Abstract: The drastic changes in the global economy, geopolitical conditions, and disruptions such as the COVID-19 pandemic have impacted the cost of living and quality of life.
By Arun Kumar Selvaraj, Tanay Panat, Rohitash Chandra
arXiv:2607. 00582v1 Announce Type: cross Abstract: This study presents a comprehensive analysis of bird diversity across Sri Lanka by integrating spatial, temporal, and environmental data.
By Dilusha Chandrasiri, Maneesha Herath, Yasith Hewarathna, Muditha Herath, Gishan Bandara, Madara Mendis, Nathali Athukorala, Nisansa de Silva, Sandareka Wickramanayake
The study evaluates whether geospatial foundation models derived from 2022 satellite data can capture physical aspects of place that conventional area-based social risk indices miss. Using LightGBM on data from 82,646 census tracts, the models moderately predicted certain survey variables and explained up to 54% of the residual variance in 40 health outcomes, notably improving predictions for annual checkups, arthritis, and high blood pressure. The models’ explanatory power increased with larger tract sizes, suggesting they add valuable, health-relevant information beyond traditional social risk measures.
By Nathaniel Hendrix, Carl Y. Zhang, Chris Heitzig, Andrew Bazemore, David H. Rehkopf
arXiv:2608. 00012v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated.
By Fengxiang Wang, Qiuyang Yu, Yueying Li, Mingshuo Chen, Chengchi Fei, Kaiyi Xu, Lixin Gu, Wangxu Wei, Junchao Gong, Lipeng Ma, Jiong Wang, Fenghua Ling, Wenlong Zhang, Xue Yang, Wenjing Yang, Ben Fei, Long Lan