{"DOI":"10.1101/2021.05.01.21256469","abstract":"AbstractThis 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.","author":[{"family":"Wang","given":"Yingfei"},{"family":"Yahav","given":"Inbal"},{"family":"Padmanabhan","given":"Balaji"}],"id":"unknown","issued":{"date-parts":[[2021,5,5]]},"publisher":"Cold Spring Harbor Laboratory","title":"Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19","type":"post"}