A paper by Bastani Hamsa, Drakopoulos Kimon, Gupta Vishal, Vlachogiannis Jon, Hadjicristodoulou Christos, Lagiou Pagona, Magiorkinis Gkikas, Paraskevis Dimitrios and Tsiodras Sotirios

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 prevalence among asymptomatic travelers, and furthermore exhibit strong country-specific idiosyncrasies. As herd immunity is still likely more than a year away [1], and travel protocols for the summer of 2021 are still being discussed, our insights raise serious concerns about internationally proposed border control policies [2] 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.

JEL Classification: I18

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

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

Source: SSRN