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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. Specifically, we describe the design of Eva’s supply chain to collect and process thousands of biological samples per day with special emphasis on capacity procurement. Then, we propose a novel, empirical Bayes estimation strategy to estimate COVID-19 prevalence among various passenger types with limited data and showcase how these estimates were instrumental in making a variety of downstream decisions. Finally, we propose a novel, multiarmed bandit algorithm that dynamically allocates tests to arriving passengers in a nonstationary environment with delayed feedback and batched decisions. All our design and algorithmic choices emphasize the need for transparent reasoning to enable human-in-the-loop analytics. Such transparency was crucial to building trust and acceptance among policymakers and public health experts in a period of global crisis.

Ketersediaan

Informasi Detail

Judul Seri

INFORMS JOURNAL ON APPLIED ANALYTICS; Vol.52 No.5 September-October 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 398-411

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EMPIRICAL BAYES
REINFORCEMENT LEARNING
LASSO
SUPPLY CHAIN DESIGN
TARGETED COVID-19 TESTING
HUMAN-IN-THE-LOOP ANALYTICS

Info Detail Spesifik

https://doi.org/10.1287/inte.2022.1128

Pernyataan Tanggungjawab

Hamsa Bastani, Kimon Drakopoulos, Vishal Gupta, Jon Vlachogiannis, Christos Hadjichristodoulou, Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis, Sotirios Tsiodras

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