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

TL;DR

This paper presents a Bayesian learning framework for designing drone-based AED delivery networks to improve emergency response, especially in rural areas, by accounting for environmental uncertainties and spatial demand patterns.

Contribution

It introduces a reliability-informed Bayesian approach for optimal drone station placement considering environmental and operational uncertainties, with a case study on cardiac arrest in Scotland.

Findings

Drone-assisted AED delivery can be cost-effective.

Optimal drone station placement depends on environmental variability.

The network improves emergency response in rural and urban areas.

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.