Efficient and Targeted COVID-19 Border Testing via Reinforcement Learning
45 Pages Posted: 26 Feb 2021 Last revised: 7 Oct 2021
Date Written: February 19, 2021
Throughout the COVID-19 pandemic, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travelers to restricting entry from select nations based on population-level epidemiological metrics such as cases, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system, nicknamed “Eva.” In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travelers infected with SARS-CoV-2, and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources based upon incoming travelers’ demographic information and testing results from previous travelers. By comparing Eva’s performance against modeled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travelers as random surveillance testing, with up to 2-4 times as many during peak travel, and 1.25-1.45 times as many asymptomatic, infected travelers as testing policies that only utilize epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travelers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.
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.
Consent: Both the GSCP and MDG committed to a high level of protection of travelers’ personal data in accordance with all EU regulations, GDPR, and Greek law. GSCP collected travelers’ personal data (with consent) via the Passenger Locator Form (PLF). * HB, KD and VG contributed equally to this work.
Keywords: COVID-19, public policy, targeted testing, contextual bandits
JEL Classification: I18
Suggested Citation: Suggested Citation