POPNET Connects with Ozan Candogan

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Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures

Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions, we study a spatial epidemic model that explicitly accounts for population movements and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%–42.4% of the baseline nonteleworkable employment level. By contrast, uniform restriction policies that achieve the same policy goal permit 3.92–6.25 times less nonteleworkable employment. Our optimization framework demonstrates the potential of targeting to limit the economic costs of unemployment while curbing the spread of an epidemic.

About Ozan Candogan

Ozan Candogan is a Professor of Operations Management at Chicago Booth. Prior to joining Booth, he was an Assistant Professor at the Fuqua School of Business where he was a member of the Decision Sciences area. He received his Ph.D. and M.S. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.

Candogan’s main research area is social and economic networks.  His research covers two complementary themes. On one hand, he investigates the impact of networks on operational decisions: He studies how to leverage network data (such as data on social networks, mobility networks, and trading networks) to improve operational decisions (ranging from pricing to inventory management and from information disclosure to facility location), and sheds light on the value of such data in different operational settings. On the other hand, he develops novel approaches and tools for the analysis of complex social and economic systems; and explores their applications to characterization of equilibria and dynamics in games, study of equilibria and comparative statics in trading networks, and design of information disclosure policies. His research has applications to operations of online social networks, ride-sharing platforms, delivery platforms, two-sided marketplaces, supply chains, and online advertising platforms, among others. 

Ozan Candogan is a recipient of the 2022 Revenue Management and Pricing Section Prize, and a finalist for the 2013 George Nicholson Student Paper Competition and the 2021 M&SOM Service Management SIG Prize. He was also a recipient of the 2009 Siebel Scholarship and the 2012 Microsoft Research Ph.D. Fellowship.