用贝叶斯方法优化无人机送除颤仪的选址，提升急救覆盖率。
A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland
- 基于患者存活概率设计贝叶斯选址模型，兼顾环境与运营不确定性。
- 在苏格兰数据上验证，可显著改善城乡长响应时间区域的覆盖。
- 评估显示该系统成本效益高，适合急救响应慢的偏远地区应用。
无人机正成为应急医疗服务体系的补充。尽管多项试点已证明无人机运送自动体外除颤器（AED）的可行性，但大规模运行仍面临高投入和环境不确定性的挑战。本文提出一种可靠性导向的贝叶斯学习框架，用于在环境与运营不确定性下设计无人机辅助AED配送网络。目标函数基于院外心脏骤停（OHCA）患者的存活概率，以确定最优无人机站点位置，并考虑现有应急医疗体系覆盖情况，提升偏远地区响应可靠性。利用苏格兰地理关联的心脏骤停数据进行验证，结果表明环境变异性和空间需求模式影响城市与农村地区最优站点布局。进一步通过基于预期质量调整生命年（QALY）的成本效益分析评估网络鲁棒性与经济可行性。研究发现，无人机辅助AED配送具备成本效益，有望显著提升急救响应覆盖范围，尤其在救护车响应时间较长的城乡区域。
原文摘要 · Abstract (English)
Drones are becoming popular as a complementary system for Emergency Medical Services (EMS). Although several pilot studies and flight trials have shown the feasibility of drone-assisted Automated External Defibrillator (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 AED delivery networks under environmental and operational uncertainty. We propose our objective function based on the survival probability of Out-off Hospital Cardiac Arrest (OHCA) patients to identify the ideal locations of drone stations. Moreover, we consider the coverage of existing 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 Quality Adjusted Life Year (QALY). The findings suggest that drone-assisted 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.
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