Multi-Objective Allocation of COVID-19 Testing Centers: Improving Coverage and Equity in Access

TL;DR

This paper presents a multi-objective optimization approach for allocating COVID-19 testing centers to enhance coverage, reduce uncertainty, and promote equity, demonstrated through case studies in Georgia.

Contribution

It introduces a novel allocation scheme that simultaneously maximizes coverage, minimizes prediction uncertainty, and reduces access inequities, improving upon existing practices.

Findings

Increased population coverage in case studies.

Improved equity of testing access.

Reduction in prediction uncertainties.

Abstract

At the time of this article, COVID-19 has been transmitted to more than 42 million people and resulted in more than 673,000 deaths across the United States. Throughout this pandemic, public health authorities have monitored the results of diagnostic testing to identify hotspots of transmission. Such information can help reduce or block transmission paths of COVID-19 and help infected patients receive early treatment. However, most current schemes of test site allocation have been based on experience or convenience, often resulting in low efficiency and non-optimal allocation. In addition, the historical sociodemographic patterns of populations within cities can result in measurable inequities in access to testing between various racial and income groups. To address these pressing issues, we propose a novel test site allocation scheme to (a) maximize population coverage, (b) minimize prediction uncertainties associated with projections of outbreak trajectories, and (c) reduce inequities in access. We illustrate our approach with case studies comparing our allocation scheme with recorded allocation of testing sites in Georgia, revealing increases in both population coverage and improvements in equity of access over current practice.