论文标题
测试谁？主动采样策略用于管理Covid-19
Whom to Test? Active Sampling Strategies for Managing COVID-19
论文作者
论文摘要
本文提出了选择个体在大流行期间（例如COVID-19）中检验感染的方法，其特征在于传染和存在无症状载体。此处介绍的智能测试想法是由机器学习中的积极学习和多臂强盗技术的动机。我们的主动采样方法与隔离策略结合使用，可以处理不同的目标，具有动态和适应性，因为它不断适应实时数据的变化。 Bandit算法使用触点跟踪，基于位置的采样和随机抽样来选择特定的个体进行测试。使用模拟纽约市的基于数据驱动的代理模型，我们表明算法样品以快速追踪感染个体的方式进行测试。实验还表明，与当前方法（例如有或没有接触式追踪的有症状的个体）相比，智能测试可以显着降低死亡率。
This 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 quarantine policies, can handle different objectives, is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. 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. Experiments also suggest that smart-testing can significantly reduce the death rates as compared to current methods such as testing symptomatic individuals with or without contact tracing.
