A goal programming model for two-stage COVID19 test sampling centers location-allocation problem.

Area of Science:

• Operations Research
• Public Health
• Epidemiology

Background:

• The COVID-19 pandemic strained healthcare systems globally, necessitating efficient resource allocation for testing.
• Anticipation of subsequent waves required strategic planning of health services, particularly for diagnostic testing.
• Decentralized testing strategies are vital to manage pandemic surges and reduce healthcare facility burden.

Purpose of the Study:

• To determine optimal locations and allocation for COVID-19 test sampling centers within a two-echelon system.
• To develop a goal programming model that minimizes total distance, number of centers, and distance to PCR laboratories.
• To provide a framework for efficient and safe COVID-19 diagnostic network planning.

Main Methods:

• A two-echelon location-allocation problem was formulated as a goal programming model.
• The model incorporated objectives for minimizing total travel distance, establishing a minimum number of test centers, and optimizing PCR lab proximity.
• The model was applied as a case study in two Turkish cities, with scenario analysis conducted.

Main Results:

• The study identified optimal locations and inventory levels for test sampling centers.
• Scenario analysis revealed that concentrating testing solely in hospitals increases contamination risk.
• The model demonstrated the feasibility of establishing an efficient testing network balancing multiple objectives.

Conclusions:

• Strategic location-allocation of COVID-19 testing centers is essential for pandemic management.
• A goal programming approach effectively balances cost, efficiency, and safety in testing network design.
• The proposed model supports informed decision-making to prevent healthcare system bottlenecks and reduce contamination risks.