Operational, Economic, and Statistical Modeling in the COVID-19 Crisis

The COVID-19 crisis revealed many fundamental structural, cultural, and operational challenges in the world. Many of these challenges, for example managing patient care in a limited resource environment, were well-known before COVID-19 and the crisis simply highlighted the importance of developing effective strategies to handle them. Others, such as the design and adherence to non-pharmaceutical mitigation strategies like lock-downs, quickly appeared as countries took differing approaches to handling the pandemic. This course will discuss how operational, economic, and statistical modeling can be used to better understand different COVID-19 responses and strategies. This is a PhD seminar that will cover prior research that can shed light onto the COVID-19 crisis, current/ongoing research that directly addresses COVID-19 pressing issues, and will also explore new research directions in this space. The course will consist of a combination of lectures by the instructors, guest lectures by researchers from all over the world, and of students' presentations of their research projects. The course will be eclectic in terms of approaches, including tools from operations research, machine learning, statistics, econometrics, and microeconomics. The course will be co-taught via live virtual sessions by Prof. Carri Chan and Prof. Gabriel Weintraub and will be available to Business, Economics, Statistics, and Engineering PhD students from Columbia and Stanford University.