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MACHINE LEARNING

We Helped Greece Build an AI System to Make Covid-19 Testing More Efficient

Kimon Drakopoulos and Vishal GuptaJanuary 27, 2021May 30, 2021
Greece
Sections
  • Business
  • Government
  • Health
  • Society
  • Technology
Topics
  • AI
  • COVID-19
  • Data Systems
  • Machine Learning
  • Pandemic
  • Reopening
  • Tourism
  • travel

Two data scientists built a machine learning system named Eva to help Greece safely reopen to nearly 80,000 tourists a day. The system, known as Eva, is nearly twice as efficient at detecting cases as random testing and it can predict spikes in other countries ten days before they show up in official case counts.

Despite government warnings, millions of Americans traveled over the holidays. Schools, universities, and workplaces face difficult decisions as people return in the midst of a coronavirus surge, potentially bringing Covid-19 with them. While vaccines are already being distributed, there’s a long road ahead until we achieve widespread vaccination and herd immunity.

What if there was a better way to determine who is most at risk for Covid-19 and who should be tested and quarantined? We’re data scientists at USC Marshall School of Business and this summer we built an artificial intelligence system to help Greece safely reopen its borders to over 80,000 tourists a day. The system, known as Eva, determined which foreign visitors to admit and who to target for testing. As schools, businesses, and tourist destinations navigate a new wave of coronavirus cases amid the busy holiday season, they should consider how to use data and artificial intelligence to deploy their testing resources efficiently and reduce the spread of the virus.

Read the full article online at The Entrepreneur.

This article was produced by Footnote in partnership with USC Marshall School of Business.

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Contributed by

Kimon Drakopoulos

Kimon Drakopoulos

Assistant Professor, Marshall School of Business
University of Southern California

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, social networks, stochastic modeling, game theory and information economics. Kimon completed his Ph.D. at the Laboratory for Information and Decision systems at MIT, focusing on the analysis and control of contagion processes on networks.

Vishal Gupta

Vishal Gupta

Associate Professor of Data Science & Operations, Marshall School of Business
University of Southern California

Vishal's research interests focus on developing novel techniques for representing uncertainty and behavior in optimization problems.

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