A Discrete Event Simulation-Based Model to Optimally Design and Dimension Mobile COVID-19 Saliva-Based Testing...

Area of Science:

• Public Health
• Epidemiology
• Operations Research

Background:

• Effective COVID-19 testing strategies are crucial for controlling viral spread.
• Mobile testing stations require efficient design and resource allocation for rapid results.
• On-campus testing presents unique logistical challenges for large populations.

Purpose of the Study:

• To address the challenge of optimal design and resource allocation for mobile COVID-19 testing stations.
• To ensure the delivery of rapid testing results to individuals.
• To develop a reusable simulation model for public health decision-making.

Main Methods:

• Development of a novel discrete event simulation model.
• Application of the model to on-campus saliva-based COVID-19 testing stations.
• Analysis of processing capacity for 10,000 samples per day using noninvasive polymerase chain reaction tests.

Main Results:

• Demonstrated the feasibility of processing 10,000 COVID-19 samples daily.
• Identified optimal configurations for mobile testing station design and resource allocation.
• Validated the model's effectiveness in a real-world university setting.

Conclusions:

• The developed discrete event simulation model provides a robust framework for optimizing mobile testing operations.
• Lessons learned offer practical insights for site managers and decision-makers in public health.
• The model's adaptability allows for application in diverse testing scenarios and contexts.