Optimization of resource-constrained policies for COVID-19 testing and quarantining

Abstract

We address the problem of controlling the COVID-19 contagion with a limited number of PCR-tests. We developed a tool that can assist policy makers in decisions as well as in justifying these decisions. Our tool consists of: A stochastic disease model, a compressed representation of interactions between people via a graph that scales well to large populations, policies for selecting PCR-testees per day, and a simulator that simulates the spread of the COVID-19 while taking into account the testing and quarantine decisions of the chosen policy. The graph model includes features that help determine the infection risk of individuals. We consider both external infection (in?icted by people outside the studied community) as well as internal infection. The graph model and known infections induce weights to people. These weights are used to select the testees per day in a greedy algorithm and in a linear-programming optimization algorithm. Our simulations indicate a reduction in total morbidity of 30–50% using the optimization algorithm compared to random sampling.A reductionof up to 40% in peakmorbidity isachieved compared to random sampling. We also studied the ef?ciency of quarantining in various policies.