[2603.23134] A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland

Computer Science > Machine Learning arXiv:2603.23134 (cs) [Submitted on 24 Mar 2026] Title:A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland Authors:Tathagata Basu, Edoardo Patelli, Gianluca Filippi, Ben Parsonage, Christy Maddock, Massimiliano Vasile, Marco Fossati, Adam Loyd, Shaun Marshall, Paul Gowens View a PDF of the paper titled A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland, by Tathagata Basu and 8 other authors View PDF Abstract:Drones are becoming popular as a complementary system for \ac{ems}. Although several pilot studies and flight trials have shown the feasibility of drone-assisted \ac{aed} delivery, running a full-scale operational network remains challenging due to high capital expenditure and environmental uncertainties. In this paper, we formulate a reliability-informed Bayesian learning framework for designing drone-assisted \ac{aed} delivery networks under environmental and operational uncertainty. We propose our objective function based on the survival probability of \ac{ohca} patients to identify the ideal locations of drone stations. Moreover, we consider the coverage of existing \ac{ems} infrastructure to improve the response reliability in remote areas. We illustrate our proposed method using geographically referenced cardiac arrest data from Scotland. The result shows how environmental variability and spatial demand patterns influence optimal dr...