Georgia Clinical & Translational Science Alliance (GaCTSA)

EFFECTIVE ALLOCATION OF TEST CENTERS FOR COVID-19 USING MACHINE LEARNING AND ADAPTIVE SAMPLINGAbstract: A critical task in managing and dealing with COVID-19 in communities is to perform diagnostic and/or antibody tests toidentify 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 medicalstaff. Additionally, information on the number of infected people can be used to develop probabilistic and statistical modelsto 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, schoolclosures, remote work, community lockdown, etc. Despite the importance of diagnostic testing and identification of thepositive cases, broad-scale testing is a challenging task particularly due to the limited number of test kits and resources. Ourproposed research focuses on the development machine learning-based allocation strategies for determining the optimallocation of COVID-19 test centers, including mobile and satellite centers, to minimize the local and global predictionuncertainties, maximize geographic coverage, associated with projections of spatio-temporal outbreak trajectories, and toimprove efficient identification of diseased cases.