A System for Critical Facility and Resource Optimization in Disaster Management and Planning

TL;DR

The work addresses disruptions to dialysis access during disasters by developing a data-driven, multi-objective optimization framework based on the Ant Colony System to reallocate patients and site temporary facilities. It leverages real mobility data, dialysis-center information, and hazard simulations to generate Pareto-optimal plans that balance total travel distance $f_0^{\mathbf{S}}$ and load-balancing dispersion $f_1^{\mathbf{S}}$ across hundreds of hurricane scenarios. The study presents scenario-wide and all-scenario aggregates, offering actionable insights for facility siting, capacity planning, and patient redeployment, with potential generalization to other critical care networks. This approach enables proactive resilience planning for health officials and emergency managers, supporting equitable and robust access to critical dialysis services in the face of increasing extreme weather events.

Abstract

Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is crucial to assess the vulnerabilities of critical care facilities to hazardous events. This study proposes optimization models for patient reallocation and the strategic placement of temporary medical facilities to bolster the resilience of the critical care system, with a focus on equitable outcomes. Utilizing human mobility data from Texas, we evaluate patient access to critical care and dialysis centers under simulated hazard scenarios. The proposed bio-inspired optimization model, based on the Ant Colony optimization method, efficiently reallocates patients to mitigate disrupted access to dialysis facilities. The model outputs offer valuable insights into patient and hospital preparedness for disasters. Overall, the study presents a data-driven, analytics-based decision support tool designed to proactively mitigate potential disruptions in access to critical care facilities during disasters, tailored to the needs of health officials, emergency managers, and hospital system administrators in both the private and public sectors.

Figures (13)

• Figure 1: Proposed data-driven methodology for patient-dialysis center assignment during disaster. We source multiple layers of real-world data to inform probabilistic facility & road closure simulation.
• Figure 2: Pre-hurricane (first column) & During-hurricane (second column) statistics
• Figure 3: Interactive user interface for displaying scenario results. In the main map window, a TAZ (blue shaded region) sends its patients to hospitals (H-markers connected with a blue line) according to inundation conditions (inundated roads in red).
• Figure 4: Scenario 00 - Solution with minimum distance cost
• Figure 5: Scenario 00 - Hospital-to-hospital patient re-assignment, minimum distance solution