高风险决策的可解释运筹学:设计希腊COVID-19测试系统

INFORMS J. Appl. Anal. Pub Date : 2022-09-01 DOI:10.1287/inte.2022.1128

Hamsa Bastani, K. Drakopoulos, Vishal Gupta, J. Vlachogiannis, Christos Hadjichristodoulou, P. Lagiou, G. Magiorkinis, Dimitris Paraskevis, S. Tsiodras

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引用次数: 3

摘要

2020年夏天，我们与希腊政府合作，设计并部署了eva，这是第一个全国性的针对COVID-19检测的强化学习系统。在本文中，我们详细介绍了三个主要设计/算法元素的基本原理:Eva的测试供应链、估计COVID-19流行率和测试分配。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Interpretable Operations Research for High-Stakes Decisions: Designing the Greek COVID-19 Testing System

In the summer of 2020, in collaboration with the Greek government, we designed and deployed Eva—the first national-scale, reinforcement learning system for targeted COVID-19 testing. In this paper, we detail the rationale for three major design/algorithmic elements: Eva’s testing supply chain, estimating COVID-19 prevalence, and test allocation.

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INFORMS J. Appl. Anal.

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