Efficient and targeted COVID-19 border testing via reinforcement learning

Hamsa Bastani, Kimon Drakopoulos (), Vishal Gupta, Ioannis Vlachogiannis, Christos Hadjichristodoulou, Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis and Sotirios Tsiodras
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Hamsa Bastani: University of Pennsylvania
Kimon Drakopoulos: University of Southern California
Vishal Gupta: University of Southern California
Ioannis Vlachogiannis: AgentRisk
Christos Hadjichristodoulou: 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: Attikon University Hospital, Medical School, National and Kapodistrian University of Athens

Nature, 2021, vol. 599, issue 7883, 108-113

Abstract: Abstract Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all travellers to restricting entry from select nations on the basis of population-level epidemiological metrics such as cases, deaths or testing positivity rates1,2. 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 travellers infected with severe acute respiratory syndrome coronavirus 2 (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 on the basis of incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers 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 travellers as testing policies that utilize only 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 travellers 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 policies3 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.

Date: 2021
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DOI: 10.1038/s41586-021-04014-z

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