LIDS alum Kimon Drakopoulos SM '11, PhD '16 focused his doctoral research on the analytics of contagion and epidemics. When the Covid-19 pandemic hit, Kimon, now an assistant professor of Data sciences and Operations at the University of Southern California’s (USC) Marshall School of Business, felt an imperative to put his skills for analyzing epidemics to good use.

“We have been working on all of this cool data science and making these complex models, but if we don’t actually apply those analytics towards mitigation of the disease, then we are wasting these resources,” Kimon says.

Kimon, looking for an opportunity to be helpful, took note when his home country of Greece announced that it was planning to reopen its borders to travel after a strict lockdown early in the pandemic. At the time, resources for managing Covid-19 were limited. Greece, like other countries, would not be able to test every traveler for Covid-19 before letting them enter the country. Instead, policymakers would have to devise a strategy for both testing and enacting restrictions selectively in order to minimize spread of the disease without depressing travel — and its economic benefits — more than necessary. Most countries dealing with this problem were using a combination of random testing and travel restrictions based on countrywide publicly reported epidemiological statistics: travelers coming from a country with very high case numbers, or deaths, would be more likely to be tested or temporarily banned from entering the country. However, Kimon knew that that strategy would miss a lot of cases. AI could help.

“Algorithms are particularly useful for decision making in cases like this when there are complicated dynamics, limited resources, and high uncertainty,” Kimon says.

Confident that he could contribute, Kimon emailed Greek Prime Minister Kyriakos Mitsotakis to offer his assistance. Within hours, the PM had accepted his offer to meet. Soon, Kimon would find himself running major aspects of Greece’s Covid-19 border management strategy. For months, he did little else but work on this one problem. Kimon took on a variety of duties, such as managing data science and engineering teams, coordinating with other groups involved in the border screening system, and training personnel who interact with travelers. He and his coauthors Hamsa Bastani (Wharton) and Vishal Gupta (USC) spent the peak travel months of 2020 getting only two hours of sleep a night.

First, Kimon and collaborators designed Eva, a reinforcement learning system rolled out in summer 2020, that recommended which travelers to test in order to catch asymptomatic cases. Travelers coming to Greece would fill out a form with basic demographic information.

Based on this, Eva would recommend whether they should be tested. Tested travelers would quarantine for up to 48 hours until they received their results, and those results would ultimately be fed back to Eva to improve estimates and drive the next round of recommendations.

The algorithm made recommendations with two goals in mind: exploitation, trying to catch as many cases as possible from travelers whose demographics were currently deemed high risk; and exploration, testing other travelers to identify demographics that might become high risk in the near future. With a virus like Covid-19 in which cases can surge very quickly, this exploratory testing is key to an effective strategy, and is part of what’s missing in a strategy based solely on countries’ current epidemiological statistics.

Implementing Eva required careful coordination in order to protect the privacy of travelers’ health information. Kimon worked closely with government officials and lawyers to set up a system that met the requirements of the European Union’s General Data Protection Regulation (GDPR). Eva could not be fed live test results, but instead received batches of pseudonymized data that included only the demographic information deemed necessary for the algorithm to make good predictions. Kimon and his team learned to work around this sort of limitation.

“The exciting part about doing something applied, when you are thinking about real world constraints, is that all the stylized math that you already know just collapses and you have to be creative. You need to know the fundamentals, so that you can adapt them quickly and effectively,” Kimon says.

Their solutions proved effective. The researchers calculate that Eva was as much as four times better than random testing at catching asymptomatic cases during peak travel times, and up to 45% more effective than testing based on epidemiological statistics.

Optimizing Eva was only part of the researchers’ challenge. Kimon’s team also met with the COVID-19 Executive Committee of Greece twice a week to inform policymakers’ decisions on travel restrictions and testing requirements. Kimon worried at first about how well the data scientists and policymakers would be able to communicate, but he found their exchanges rewarding and came to appreciate the difficulty of balancing the data his team provided against various other factors when deciding on policies. He says that the key to good collaboration was transparency. Data scientists might like to build complex models, but when providing information to a policymaker, Kimon thinks it’s important to give them something unambiguous and actionable.

“Simple, deep insights make the world move,” Kimon says.

Eventually, as testing resources became less scarce and vaccines became available, there was no longer need for Eva’s recommendations. Over the past year, Kimon has returned to his normal life, teaching, conducting research, and getting a full night’s sleep. His current projects focus on a different sort of viral spread, that of misinformation on social networks. He remains passionate about the use of data science and analytics for governance. And after spending so long managing other people’s travels, Kimon managed to go on a trip of his own. He and his wife (fellow LIDS alum Yola Katsargyri SM’08, PhD ’17) spent a month in Alaska, an experience he describes as incredible. Kimon does not miss the exhaustion, media attention, or disruption to his life that came with his time working on Covid-19, but he remembers the experience with gratitude.

“The opportunity to work on a high impact project like this is rare, perhaps once in a lifetime, and I feel blessed that I had the chance,” Kimon says. “I am extremely proud of what my team accomplished.”