论文标题
“合并”与预测模型的组合可以减少73％的COVID-19（电晕）测试数量
A combination of 'pooling' with a prediction model can reduce by 73% the number of COVID-19 (Corona-virus) tests
论文作者
论文摘要
我们表明，将预测模型（基于神经网络）与一种新的测试合并方法（比原始的Dorfman方法更好，并且比双重功能更好），称为“网格”，我们可以将COVID-19测试的数量减少73％。
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%.
