论文标题
Crackovid：优化组测试
Crackovid: Optimizing Group Testing
论文作者
论文摘要
我们研究了在Covid-19的背景下通常称为小组测试的问题。鉴于从患者那里采集的$ N $样品，我们应该如何选择要测试的样品混合物，以最大程度地提高信息并最大程度地减少测试数量？我们考虑适应性和非自适应策略，并采取贝叶斯的方法，同时感染患者和测试错误。我们首先提出一个基于信息理论的数学上有原则的目标。然后，我们使用遗传算法优化非自适应优化策略，并利用自适应亚模型的数学框架来获得贪婪自适应方法的理论保证。
We study the problem usually referred to as group testing in the context of COVID-19. Given $n$ samples taken from patients, how should we select mixtures of samples to be tested, so as to maximize information and minimize the number of tests? We consider both adaptive and non-adaptive strategies, and take a Bayesian approach with a prior both for infection of patients and test errors. We start by proposing a mathematically principled objective, grounded in information theory. We then optimize non-adaptive optimization strategies using genetic algorithms, and leverage the mathematical framework of adaptive sub-modularity to obtain theoretical guarantees for the greedy-adaptive method.
