Balancing access, precision, and equity in adaptive test site allocation with an application to COVID-19 in Atlanta, Georgia.

Emergency pandemic disease surveillance encompasses a suite of public health data-based measures to monitor and prevent further spread of disease. Early in the COVID-19 pandemic, one important method for monitoring local spread of infection involved the deployment of local testing sites. However, key concerns for the accuracy and completeness of any surveillance system include local access to testing sites, precision in the estimates of disease incidence and prediction of its spatiotemporal trajectory, and racial equity in testing availability, concerns often not rigorously taken into account in public health policy-making, especially during a public health emergency. In addition, the rapid local transmission dynamics of an infectious disease outbreak often require methods able to react to spontaneous hotspots and disease clusters in or near real-time. To address these competing objectives, we integrate Bayesian spatiotemporal disease modeling using COVID-19 case data into a multi-objective optimization solved by an interior-point approach. We show the adaptive multi-objective method outperforms non-adaptive test-site allocation in terms of time and resources required, and sacrifices minimal performance compared to methods optimizing a single objective criteria. We hope our method can be used to improve test-site allocation procedures for future local and seasonal outbreaks and broader pandemics.