A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland
🤖 AI Summary
This study addresses the critical challenge of delayed automated external defibrillator (AED) delivery in out-of-hospital cardiac arrest emergencies by proposing a reliability-driven Bayesian optimization framework for the cost-effective deployment of drone-based AED networks under environmental and operational uncertainties. The approach uniquely prioritizes patient survival probability as the central optimization objective, integrating Bayesian learning to model spatiotemporal uncertainties while synergistically leveraging existing emergency infrastructure and accounting for urban–rural disparities across Scotland. Through spatial layout optimization, cost-effectiveness analysis, and quality-adjusted life year (QALY) evaluation, the proposed solution substantially enhances emergency coverage in remote and response-delayed regions, demonstrating both high cost-effectiveness and robust network performance.
Application Category
📝 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 drone station placement across urban and rural regions. In addition, we assess the robustness of the network and evaluate its economic viability using a cost-effectiveness analysis based on expected \ac{qaly}. The findings suggest that drone-assisted \ac{aed} delivery is expected to be cost-effective and has the potential to significantly improve the emergency response coverage in rural and urban areas with longer ambulance response times.
Problem
Research questions and friction points this paper is trying to address.
drone coverage network
AED delivery
environmental uncertainty
emergency medical services
out-of-hospital cardiac arrest
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian learning
drone coverage network
AED delivery
survival probability optimization
cost-effectiveness analysis
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