论文标题
通过贝叶斯优化在流行病学和社会经济上的最佳政策上
Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
论文作者
论文摘要
大规模公共隔离，俗称锁定，是一种非药物干预措施，用于检查疾病的传播。本文介绍了ESOP（在流行病学和社会经济上最佳政策），这是一种使用贝叶斯优化的活跃机器学习技术的新应用，与流行病学模型相互作用，以达到锁定时间表，以最佳地平衡公共卫生福利和社会经济福利和社会经济经济性降低锁定经济性降低的距离，在锁定期间降低了损失的经济性降低。使用Viper（病毒个体 - 个体 - 政策环境）的案例研究证明了ESOP的实用性，这是本文也提出的基于随机剂的模拟器。但是，ESOP足够灵活，可以以黑盒方式与任意流行病学模拟器进行交互，并产生涉及多个锁定阶段的时间表。
Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER (Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that this paper also proposes. However, ESOP is flexible enough to interact with arbitrary epidemiological simulators in a black-box manner, and produce schedules that involve multiple phases of lock-downs.
