  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 quarantine
policies, can handle different objectives, is dynamic and adaptive in the sense
that it continually adapts to changes in real-time data. The bandit algorithm
uses contact tracing, location-based sampling and random sampling in order to
select specific individuals to test. 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. Experiments also suggest
that smart-testing can significantly reduce the death rates as compared to
current methods such as testing symptomatic individuals with or without contact
tracing.
