Graph-AI Crisis Medical Resource Allocation: Predictive Population-at-Risk Modeling with Elliott Wave Load Forecasting and Autonomous Network Optimization

Abstract

Mass casualty incidents expose critical gaps in healthcare system readiness. Current triage protocols (START, SALT) classify patients but do not allocate resources; AI systems optimize single facilities but ignore network-level distribution. This paper presents Graph-AI Crisis Medical Resource Allocation, a comprehensive framework combining: (1) Population-at-Risk PDFs calibrated from historical incident-fatality ratios, (2) Elliott Wave decomposition for temporal casualty prediction with Peak of Control (POC) identification, (3) Graph Neural Network allocation for autonomous bed/workforce/equipment distribution, and (4) Crisis Readiness Index (CRI)—a novel composite indicator of system robustness. Validation on two real-world calibrated scenarios demonstrates: Scenario A (Multi-Train Collision, 935 patients, 0.56:1 capacity ratio): 100% vs 81.6% admission (+18.4pp, zero rejections), peak occupancy 66.3% vs 100%. Scenario B (M7.2 Earthquake, 7,124 patients, 4.3:1 capacity ratio): 52.5% vs 43.9% admission (+8.6pp, 449 additional lives saved). All simulation results calibrated against EM-DAT earthquake database (15 events), NTSB/ERA transport records (13 events), and HAZUS-MH damage-to-casualty conversion rates.

Introduction

The text presents Graph-AI, a predictive crisis management framework designed to improve healthcare response during mass casualty incidents such as train collisions and earthquakes. Existing emergency systems are largely reactive and fragmented, focusing only on patient severity classification without optimizing hospital allocation, resource balancing, or demand forecasting.

Traditional systems such as START, SALT, and mSTART classify patients into severity categories (red, yellow, green, black) but do not determine which hospital patients should be sent to. AI-based triage systems optimize decisions within a single hospital but lack network-wide coordination, while operations research models assume static demand and cannot adapt dynamically during crises.

To solve these limitations, Graph-AI introduces a three-stage predictive crisis management framework:

1. Population and Casualty Prediction
   Before incidents occur, the system builds probability density models using historical disaster data. When a crisis happens, expected casualties are estimated based on exposed population and historical injury ratios.
2. Facility and Workforce Activation
   The framework automatically activates sleeper facilities such as private clinics, dental practices, and veterinary centers while mobilizing off-duty staff through a sigmoid-based activation curve that responds to forecasted demand.
3. Graph-Based Allocation and Dynamic Rebalancing
   A graph neural network continuously allocates patients across hospitals based on travel time, available capacity, staffing, equipment, and case specialization. The system dynamically rebalances patients as conditions change.

A major innovation is the application of Elliott Wave theory, borrowed from financial market analysis, to model patient arrival waves during disasters. Casualties tend to arrive in clustered surges similar to trading volume peaks in financial markets. By predicting future “Peak Admission Points” (PAPs), the system can proactively reposition ICU staff, beds, and equipment several hours before patient surges occur.

The framework also introduces a Crisis Readiness Index (CRI), which measures healthcare system preparedness using:

• Bed availability
• Workforce availability
• Equipment sufficiency

CRI categorizes system status into:

• Robust (≥70%)
• Stressed (40–69%)
• Critical (<40%)

The methodology combines:

• Statistical casualty prediction models
• Elliott Wave temporal decomposition
• Graph-based hospital optimization
• Dynamic sleeper facility activation

Two experimental scenarios were tested:

Scenario A: Multi-Train Collision

• 3,500 exposed passengers
• Estimated 935 injuries
• Network of 8 hospitals + 4 sleeper facilities

Results showed Graph-AI achieved:

• 100% patient admission success
• Zero rejections
• Lower occupancy stress
• Better CRI stability
• Reduced operational cost

Compared to baseline systems, Graph-AI improved admissions from 81.6% to 100% through intelligent allocation and proactive resource activation.

Scenario B: Major Earthquake

• M7.2 earthquake affecting 500,000 people
• Estimated 7,124 survivors needing care
• Severe infrastructure limitations

This scenario tested the framework under extreme overload conditions where demand far exceeded available capacity.

Conclusion

Graph-AI Crisis Medical Resource Allocation addresses the full lifecycle of mass casualty response: predictive casualty estimation via Population-at-Risk PDFs, temporal load forecasting via Elliott Wave with POC identification, autonomous network allocation via Graph Neural Network optimization, and real-time robustness assessment via Crisis Readiness Index. Validation on two real-world calibrated scenarios demonstrates: Scenario A (Multi-Train Collision, adequate capacity): 100% vs 81.6% admission (+18.4pp), zero rejections, peak occupancy 66.3% vs 100%. Scenario B (M7.2 Earthquake, overwhelmed capacity): 52.5% vs 43.9% admission (+8.6pp), 449 additional lives saved. Graph-AI overcomes six specific limitations of previous systems: (1) triage systems that classify but do not allocate, (2) AI systems that optimize single facilities without network awareness, (3) OR models that assume static demand, (4) lack of demand forecasting capability, (5) lack of network-level optimization, and (6) lack of a forward-looking robustness metric. The framework is not a panacea for infrastructure inadequacy (Scenario B honestly demonstrates this), but it is necessary infrastructure for optimal use of whatever capacity exists. Together with capacity investment, Graph-AI enables healthcare systems to be demonstrably more robust in mass casualty incidents. The 449 additional lives saved in Scenario B represent real clinical value even when infrastructure is overwhelmed.

References

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Copyright

Copyright © 2026 Jones Andrews, Dr. Terry Jacob. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.