Interpretable Operations Research for High-Stakes Decisions: Designing the Greek COVID-19 Testing System-Reference-Cited by-同舟云学术

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

Published:2022-09 Issue:5 Volume:52 Page:398-411
ISSN:2644-0865
Container-title:INFORMS Journal on Applied Analytics
language:en
Short-container-title:INFORMS Journal on Applied Analytics

Author:

Bastani Hamsa¹^ORCID,Drakopoulos Kimon²^ORCID,Gupta Vishal²^ORCID,Vlachogiannis Jon³^ORCID,Hadjichristodoulou Christos⁴^ORCID,Lagiou Pagona⁵^ORCID,Magiorkinis Gkikas⁵^ORCID,Paraskevis Dimitrios⁵^ORCID,Tsiodras Sotirios⁵^ORCID

Affiliation:

1. Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;

2. University of Southern California Marshall School of Business, Los Angeles, California 90089;

3. AgentRisk, San Francisco, California;

4. University of Thessaly, Volos 382 21, Greece;

5. National and Kapodistrian University of Athens, Athens 157 72, Greece

Abstract

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.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Link

https://pubsonline.informs.org/doi/pdf/10.1287/inte.2022.1128

Reference25 articles.

1. Making Better Fulfillment Decisions on the Fly in an Online Retail Environment

2. Online Decision Making with High-Dimensional Covariates

3. Efficient and targeted COVID-19 border testing via reinforcement learning

4. A Learning Approach for Interactive Marketing to a Customer Segment

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