Σεμινάριο CEID & Social Hour: "Efficient and targeted Covid-19 border testing via reinforcement learning" , Dr. Kimon Drakopoulos, Assistant Professor, USC Marshall School of Business, ΗΠΑ.

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Title: Efficient and targeted Covid-19 border testing via reinforcement learning
 
 
Time & Place: 17-12-2021  16:00-18:00
 
 
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 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 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 se
rious 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.
 
About the speaker  Kimon Drakopoulos is an Assistant Professor in the Data Sciences and Operations  department at USC Marshall School of Business. His research focuses on the operations of complex networked systems, epidemics, social networks, stochastic modeling, game theory and information economics. Kimon, prior to joining USC, completed his PhD at the Laboratory for Information and Decision systems at MIT focusing on the analysis and control of contagion processes on networks. From April 2020 - December 2020 he worked with the Greek COVID-19 Scientific taskforce on deploying data-driven strategies for the containment of the COVID-19 Pandemic. He has been awarded the Wagner Prize for excellence in Applied Analytics and the Pierskalla Award for contributions to Healthcare analytics. 

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