The Opioid Epidemic: Using OR/MS to combat the leading public health crisis

The opioid epidemic is a decades long public health crisis that is only continuing to grow in severity and impact. It is currently the foremost public health crisis in the United States, with its roots tracing back to mid to late 1990s when the highly concentrated prescription pain killer Oxycontin was approved by the U.S. Food and Drug Administration in conjunction with the controversial pain as the fifth vital sign campaign, among other factors.

As the U.S. government, healthcare system, and general public struggle to combat this epidemic, members of the OR/MS and analytics community are leveraging data and research to help provide support and solutions.

Joining me to share their unique insight into a data-driven approach to help combat the opioid epidemic are Joyce Luo with MIT and Bartolomeo Stellato with Princeton University. Their study, “Frontiers in Operations: Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic,” was recently published in the INFORMS journal Manufacturing & Service Operations Management.

As we know in many episodes of this podcast, operations research and management science can really help by offering powerful tools to deal with societal problems at various micro and macro scales. So what we present with the opioid epidemic is just one example of the many issues we could tackle using two of the tools adopted in this work, and the first one is estimating dynamics and behavior of crises and epidemics over time, and the second one is integrating such models with advanced decision-making tools based on optimization.

Interviewed this episode:

Joyce Luo


Joyce Luo is a rising third-year PhD student at the MIT Operations Research Center, advised by Professor Georgia Perakis. Her research integrates optimization and machine learning to address challenges in healthcare and service operations management, with a particular focus on public health and hospital operations. Her doctoral research is supported by an NSF Graduate Research Fellowship. Before joining the PhD program, she received a BSE in Operations Research and Financial Engineering from Princeton University with minors in Computer Science and Statistics & Machine Learning.

Bartolomeo Stellato

Princeton University

Bartolomeo Stellato is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University. Previously, he was a Postdoctoral Associate at the MIT Sloan School of Management and Operations Research Center. He received a DPhil (PhD) in Engineering Science from the University of Oxford, a MSc in Robotics, Systems and Control from ETH Zürich, and a BSc in Automation Engineering from Politecnico di Milano. He is the developer of OSQP, a widely used solver in mathematical optimization. Bartolomeo Stellato‘s awards include the NSF CAREER Award, the Princeton SEAS Howard B. Wentz Jr. Faculty Award, the Franco Strazzabosco Young Investigator Award from ISSNAF, the Princeton SEAS Innovation Award in Data Science, the Best Paper Award in Mathematical Programming Computation, and the First Place Prize Paper Award in IEEE Transactions on Power Electronics. His research focuses on data-driven computational tools for mathematical optimization, machine learning, and optimal control.

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