EFFECTIVE ALLOCATION OF TEST CENTERS FOR COVID-19 USING MACHINE LEARNING AND ADAPTIVE SAMPLING ABSTRACT A critical task in managing and dealing with COVID-19 in communities is to perform diagnostic and/or antibody tests to identify diseased individuals. This information is critical to public health officials to estimate prevalence and transmission, and to effectively plan for required resources such as ICU beds, ventilators, personal protective equipment, and medical staff. Additionally, information on the number of infected people can be used to develop probabilistic and statistical models to estimate the reproduction number of the disease, and to predict the likely spatial and temporal trajectories of the outbreak. This provides vital information for planning actions and preparing policies and guidelines for social-distancing, school closures, remote work, community lockdown, etc. Despite the importance of diagnostic testing and identification of the positive cases, broad-scale testing is a challenging task particularly due to the limited number of test kits and resources. Our proposed research focuses on the development machine learning-based allocation strategies for determining the optimal location of COVID-19 test centers, including mobile and satellite centers, to minimize the local and global prediction uncertainties, maximize geographic coverage, associated with projections of spatio-temporal outbreak trajectories, and to improve efficient identification of diseased cases.
EFFECTIVE ALLOCATION OF TEST CENTERS FOR COVID-19 USING MACHINE LEARNING AND ADAPTIVE SAMPLING NARRATIVE Diagnostic and antibody tests for COVID-19 can provide invaluable information on prevalence and transmission of the disease. However, due to limited test capacity, broad-scale testing is currently not feasible. Consequently, there is a pressing need for a systematic and data-driven approach to defining testing strategies, in particular, determining the number and location of satellite and mobile testing centers (e.g., drive-through test locations). Our research program develops machine learning approaches to effectively allocate test centers for COVID-19 at the city, county, and state levels to accurately and reliably estimate the disease prevalence and its trajectory for resource planning and policy making, and to efficiently identify cases for treatment.
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