A combination of 'pooling' with a prediction model can reduce by 73% the number of COVID-19 (Corona-virus) tests
Tomer Cohen, Lior Finkelman, Gal Grimberg, Gadi Shenhar, Ofer Strichman, Yonatan Strichman, Stav Yeger
arXiv:2005.03453v31 citations
Originality Incremental advance
AI Analysis
This addresses the challenge of efficient COVID-19 testing for public health systems, though it appears incremental as it builds on existing pooling methods.
The authors tackled the problem of reducing the number of COVID-19 tests needed by combining a neural network prediction model with a new pooling method called 'Grid', achieving a 73% reduction in tests.
We show that combining a prediction model (based on neural networks), with a new method of test pooling (better than the original Dorfman method, and better than double-pooling) called 'Grid', we can reduce the number of Covid-19 tests by 73%.