A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach

no code implementations • 28 Jul 2020 • Hagit Grushka-Cohen, Raphael Cohen, Bracha Shapira, Jacob Moran-Gilad, Lior Rokach

We find that individuals can be ranked for effective testing using a few simple features, and that ranking them using such models we can capture 65% (CI: 64. 7%-68. 3%) of the positive individuals using less than 20% of the testing capacity or 92. 1% (CI: 91. 1%-93. 2%) of positives individuals using 70% of the capacity, allowing reserving a significant portion of the tests for population studies.

Decision Making Multi-Armed Bandits