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

Tathagata Basu, Edoardo Patelli, Gianluca Filippi, Ben Parsonage, Christy Maddock, Massimiliano Vasile, Marco Fossati, Adam Loyd, Shaun Marshall, Paul Gowens

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.

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

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.
Paper Structure (73 sections, 58 equations, 9 figures, 14 tables, 1 algorithm)

This paper contains 73 sections, 58 equations, 9 figures, 14 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Proposed framework
  3. Paper Organisation
  4. Surrogate Models for Uncertainty Propagation
  5. Surrogate Modelling Strategy
  6. Validation Metrics
  7. Ambulance Response Surrogate
  8. Model diagnostics
  9. Interpretation
  10. Wind Surrogates
  11. Wind Speed
  12. Estimated kernel parameters.
  13. Wind Direction
  14. Estimated kernel parameters for wind speed.
  15. Drone Surrogates
  16. ...and 58 more sections

Figures (9)

  • Figure 1: Prior predictive distribution on total number of active sites for NHS GGC (left) for $\theta_0=-1$ and for NHS Grampian (right) for $\theta_0=-2$.
  • Figure 2: Summary of our analyses with NHS GGC. The upper-left panel shows number of sites and QALY gain against $\beta$; the upper-right panel shows mean drone response time and number of cases covered in 6 and 8 mins; the middle-left panel shows the number of cases where drone reaches before ambulance; the middle-right panel shows the final network design; the lower-left panel shows the cost-effectiveness of the designs; and the lower-right panel shows the coverage reliability of the designs
  • Figure 3: Summary of our analyses with NHS Grampian. The upper-left panel shows number of sites and QALY gain against $\beta$; the upper-right panel shows mean drone response time and number of cases covered in 6 and 8 mins; the middle-left panel shows the number of cases where drone reaches before ambulance; the middle-right panel shows the final network design; the lower-left panel shows the cost-effectiveness of the designs; and the lower-right panel shows the coverage reliability of the designs
  • Figure 4: Scatterplot and correlation plot for the ambulance response time dataset.
  • Figure 5: Scatterplot and correlation plot for wind models.
  • ...and 4 more figures