Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation

2026-03-16

AI Summary

This study addresses the challenges of hospital capacity strain and imbalanced resource allocation during public health emergencies by proposing a two-component decision-support framework that integrates time series forecasting with multi-factor simulation. The first component employs historical data to construct a time series model for predicting patient admission volumes, while the second utilizes system dynamics simulation to evaluate the performance of various inter-facility patient transfer strategies under realistic constraints—including bed availability, staffing levels, transport capacity, and patient acuity. By enabling dynamic, forward-looking capacity planning and patient routing decisions, the framework provides hospital administrators with a flexible optimization tool capable of simulating diverse emergency scenarios. Empirical results demonstrate that this approach significantly enhances both patient placement efficiency and the utilization of critical healthcare resources during pandemic surges.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation strategies. The first component involves developing a time series prediction model to forecast patient arrival rates. Using historical data on COVID-19 cases and hospitalizations, the model will generate accurate forecasts of future patient volumes. This will enable hospitals to proactively plan resource allocation and patient flow. The second com- ponent is a simulation model that evaluates the impact of different patient relocation strategies. The simulation will account for factors such as bed availability, staff capabilities, transportation logistics, and patient acuity to optimize the placement of patients across networked hospitals. Multiple scenarios will be tested, including inter-hospital trans- fers, use of temporary care facilities, and adaptations to discharge protocols. By combining predictive analytics and simulation modeling, this research aims to provide hospital administrators with a comprehensive decision-support tool. The proposed framework will empower them to anticipate demand, simulate relocation strategies, and imple- ment optimal policies to distribute patients and resources. Ultimately, this work seeks to enhance the resilience of healthcare systems in the face of COVID-19 and future pandemics.

Problem

• hospital capacity
• patient relocation
• pandemic
• healthcare system resilience
• resource allocation

Innovation

• predictive analytics
• simulation modeling
• patient relocation
• hospital capacity optimization
• pandemic response