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Systematic Comparison of Different Compartmental Models for Predicting COVID-19 Progression.

Epidemiologia (Basel, Switzerland)·2025
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An Optimization-Based Framework to Dynamically Schedule Hospital Beds in a Pandemic.

Marwan Shams Eddin1, Hussein El Hajj2

  • 1Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA.

Healthcare (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study presents an optimization framework to dynamically schedule hospital beds during pandemics, reducing healthcare costs by over 50% by minimizing patient rejections and improving resource allocation.

Keywords:
healthcare operationshospital bed schedulingpandemicresource sharingrobust optimization

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Area of Science:

  • Operations Research
  • Public Health
  • Epidemiology

Background:

  • Pandemics strain hospital capacity, increasing mortality and costs.
  • Effective resource management is crucial for healthcare system resilience.

Purpose of the Study:

  • Develop an optimization framework for dynamic hospital bed scheduling.
  • Minimize total healthcare costs, including patient rejections and logistics.

Main Methods:

  • Integrated standard beds, buffer capacity, field hospitals, and transfers.
  • Linked SEIRD epidemic forecasting with robust optimization for demand uncertainty.
  • Reformulated problem for computational efficiency and derived structural properties.

Main Results:

  • Framework reduced healthcare costs by over 50% in a COVID-19 case study.
  • Significant cost reduction achieved by lowering patient rejection rates.
  • Provided guidance on opening field hospitals and allocating buffer beds.

Conclusions:

  • Combining epidemic forecasting with prescriptive optimization enhances crisis response.
  • The framework improves healthcare system resilience and informs policy.
  • Dynamic bed scheduling is vital for managing pandemic surges effectively.