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.
