RESUMEN
This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with vaccination and quarantine policies and is adaptive to changes in real-time data. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. The results show that smart-testing is effective in significantly reducing infection and death rates as compared to current policies, with or without vaccination.