Determining optimal COVID-19 testing center locations and capacities.

Current public health systems face challenges in managing sudden increases in testing demand. Existing research has shown that static testing center setups may not adapt well to fluctuating needs. Prior studies have focused on general facility location problems, but not specifically for infectious disease surges. No prior work had resolved how to dynamically adjust testing capacity during a pandemic. The gap in real-time testing center optimization motivated this study. Researchers propose a need for models that incorporate both location and capacity decisions. This paper introduces a method to address these limitations. The approach aims to improve accessibility while managing costs and delays.

This study aims to develop a model for optimizing testing center locations and capacities during surges. The specific problem is how to allocate resources efficiently in a dynamic environment. Researchers wanted to test whether a multi-period approach could outperform static models. The motivation stems from the need to reduce delays and bottlenecks in testing. The study focuses on the Region of Waterloo as a case example. The model considers budget, capacity, and turnaround time constraints. The goal is to provide actionable insights for public health officials. The approach combines location and capacity decisions in a single framework.

The researchers designed a two-echelon multi-period model for testing center optimization. The model includes both existing and pop-up testing centers as decision variables. Demand regions are assigned to centers based on proximity and capacity. The model accounts for changes in demand over multiple time periods. Constraints include budget limits and lab turnaround times. The objective function minimizes delayed appointments and specimens. The model was applied to data from the Region of Waterloo in Canada. Sensitivity analyses tested the model under uncertain demand scenarios.

The model identified optimal locations for pop-up testing centers in densely populated areas. Results showed that dynamic capacity adjustments reduced delays by up to 39%. The optimal strategy involved expanding existing centers and adding pop-ups. Pop-up locations were selected based on proximity to labs and population density. The model outperformed static approaches in high-demand scenarios. Sensitivity analyses confirmed the robustness of the model under uncertainty. The study found that timing of capacity expansions is critical for performance. The results suggest that strategic placement improves overall testing efficiency.

The authors propose that dynamic capacity planning improves testing accessibility during surges. The model provides a framework for public health decision-makers to use. The results suggest that pop-up centers should be placed in high-density areas. The study emphasizes the importance of timing in capacity expansions. The two-echelon model outperformed static approaches in cost and efficiency. The findings support the use of multi-period planning in public health logistics. The model can be adapted to other regions with similar constraints. The authors suggest that the approach can be extended to other healthcare resource allocation problems.