Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border

32 Pages Posted: 26 Feb 2021 Last revised: 29 Jul 2021

See all articles by Hamsa Bastani

Hamsa Bastani

University of Pennsylvania - The Wharton School

Kimon Drakopoulos

University of Southern California

Vishal Gupta

Data Science and Operations, Marshall School of Business

Jon Vlachogiannis

AgentRisk

Christos Hadjicristodoulou

University of Thessaly

Pagona Lagiou

National and Kapodistrian University of Athens

Gkikas Magiorkinis

National and Kapodistrian University of Athens

Dimitrios Paraskevis

National and Kapodistrian University of Athens

Sotirios Tsiodras

National and Kapodistrian University of Athens - 4th Department of Internal Medicine

Date Written: February 19, 2021

Abstract

On July 1st, 2020, members of the European Union gradually lifted earlier COVID-19 restrictions on non-essential travel. In response, we designed and deployed “Eva” – a novel, self-learning artificial intelligence system – across all Greek borders to identify asymptomatic travelers infected with SARS-CoV-2 based on demographic characteristics and results from previously tested travelers. Eva allocates Greece’s limited testing resources to (i) limit the importation of new cases and (ii) provide real-time estimates of COVID-19 prevalence to inform border policies.

Counterfactual analysis shows that our system identified on average 1.85x as many asymptomatic, infected travelers as random surveillance testing, and up to 2-4x as many during peak travel. Moreover, for most countries, Eva identified atypically high prevalence 9-days earlier than machine learning systems based on publicly reported data. By adaptively adjusting border policies 9-days earlier, Eva prevented additional infected travelers from arriving.

Finally, using Eva’s unique cross-country, large-scale dataset on prevalence in asymptomatic populations, we show that commonly used public data on cases/deaths/testing have limited predictive value for the actual prevalence among asymptomatic travelers, and furthermore exhibit strong country-specific idiosyncrasies. As herd immunity is still likely more than a year away, and travel protocols for the summer of 2021 are still being discussed, our insights raise serious concerns about internationally proposed border control policies that are both country-agnostic and solely based on public data. Instead, our work paves the way for leveraging AI and real-time data for public health goals, such as border control during a pandemic.

Note: Funding Statement: V.G. was partially supported by the National Science Foundation through NSF Grant CMMI-1661732.

Declaration of Interests: H.B., V.G., and J.V. declare no conflict of interest. K.D. declares non-financial competing interest as an unpaid Data Science and Operations Advisor to the Greek Government from May 1st, 2020 to Nov 1st, 2020. C.H., P.L., G.M., D.P., and S.T. declare non-financial competing interest as members of the Greek National COVID-19 Taskforce.

* HB, KD and VG contributed equally to this work.

Keywords: COVID-19, public policy, targeted testing, contextual bandits

JEL Classification: I18

Suggested Citation

Bastani, Hamsa and Drakopoulos, Kimon and Gupta, Vishal and Vlachogiannis, Jon and Hadjicristodoulou, Christos and Lagiou, Pagona and Magiorkinis, Gkikas and Paraskevis, Dimitrios and Tsiodras, Sotirios, Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border (February 19, 2021). Available at SSRN: https://ssrn.com/abstract=3789038 or http://dx.doi.org/10.2139/ssrn.3789038

Hamsa Bastani (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Kimon Drakopoulos

University of Southern California ( email )

Marshall School of Business
Los Angeles, CA 90089
United States

Vishal Gupta

Data Science and Operations, Marshall School of Business ( email )

Marshall School of Business
BRI 401, 3670 Trousdale Parkway
Los Angeles, CA 90089
United States

HOME PAGE: http://www-bcf.usc.edu/~guptavis/

Jon Vlachogiannis

AgentRisk ( email )

Christos Hadjicristodoulou

University of Thessaly ( email )

Gaiopolis Campus
Larissa, 41110
Greece

Pagona Lagiou

National and Kapodistrian University of Athens ( email )

Gkikas Magiorkinis

National and Kapodistrian University of Athens ( email )

Dimitrios Paraskevis

National and Kapodistrian University of Athens ( email )

Sotirios Tsiodras

National and Kapodistrian University of Athens - 4th Department of Internal Medicine

Greece

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