Demand-capacity estimation using queueing theory: application to hospital resource planning in the 2023 Türkiye...

Area of Science:

• Disaster Medicine
• Mathematical Modeling
• Public Health Preparedness

Background:

• Earthquakes pose significant challenges to healthcare infrastructure and demand.
• Accurate forecasting of medical personnel is crucial for effective disaster response.
• Previous models often lack validation with real-world disaster data.

Purpose of the Study:

• To develop and validate a mathematical model for estimating emergency physician requirements in post-earthquake scenarios.
• To utilize actual data from the 2023 Kahramanmaraş earthquakes in Türkiye for model development and validation.
• To provide a tool for effective physician workforce planning in disaster situations.

Main Methods:

• A five-step framework was employed, analyzing population impact, building vulnerability, and casualty estimations.
• Empirical fragility functions were used to determine collapse probabilities based on earthquake intensity.
• The M/M/s queuing theory model was applied to calculate physician demand, considering patient arrival rates and examination times.

Main Results:

• The model predicted 11,645 emergency department visits in Hatay province within 144 hours post-earthquake.
• An estimated 27 emergency physicians per shift (81 total) were required to meet the projected demand.
• Model estimations closely aligned with actual post-disaster hospital admission data, confirming its validity.

Conclusions:

• A scalable, data-validated model for disaster physician workforce planning has been developed.
• The queuing-based approach facilitates strategic resource allocation and enhances health system resilience.
• This model's integration of field-specific data offers real-time health system needs forecasting.