On solving the patient bed assignment problem in pandemic situations

Patient admission is a routine task in healthcare facilities, but during pandemics like COVID-19 and Mpox, hospital management becomes significantly more complex. Consequently, hos- pitals may struggle to meet all patient demands, especially when the number of available beds is limited compared to the number of incoming patients. During the COVID-19 pandemic, the Tunisian Ministry of Health implemented strict assignment protocols, prioritizing patients based on severity to ensure timely care and efficient resource use. However, despite numerous efforts and resource allocation, Tunisian hospitals remained unprepared for large-scale emergencies, highlighting ongoing challenges in resource management. This paper investigates the patient bed assignment problem in pandemic sit- uations and proposes a linear programming model to optimize resource allocation while minimizing patient rejections, bed transfers, and associated costs. A sensitivity analysis is conducted to assess the impact of various input parameters on the overall assignment costs. The model is validated using real data from Tunisian hospitals, demonstrating its effectiveness in improving hospital admission capacity.