COVID-19 Forecasting: Three Cheers for Simple Models
SDP Free Webinar
Speaker: Eric Bickel, Professor, The University of Texas at Austin.
Moderator: Carl Spetzler, CEO, Strategic Decisions Group.
Sept 2nd, 2020 at 8:00 am PT / 11:00 am ET
Over the last six months we have witnessed policymakers grappling with how to respond to the spread of COVID-19 across the globe. In the United States, policymakers at local, state, and federal levels have faced difficult decisions regarding the degree to which citizens should interact with each other, how much of the economy should be curtailed, and how to allocate scarce testing and hospital resources. These decisions have been informed and guided by a set of epidemiological models.
In this talk, we analyze the performance of the models used to forecast the spread of COVID-19 and relate differences in performance to differing modeling approaches and structures. For example, some COVID-19 models are “bottom-up” and model the interactions between individuals and communities in detail (i.e., SIR models). While other models are “top-down” and attempt to capture the high-level dynamics of the spread. Some models include uncertainty, while others are deterministic. Certain models are designed to inform policy decisions, while others are meant to provide forecasts.
We compare the performance of these models to a simple (two-equation) model that we have used to forecast the spread of COVID-19 at the national, state, and local level. Surely large models with hundreds of equations backed by a team of experts should outperform a simple model that has three inputs and runs in Excel. As we discuss, a few COVID-19 models do achieve this level of success, but most do not.
We will discuss this apparent paradox and the implications for decision analysis.
Eric J. Bickel is a professor and director of the Graduate Program in Operations Research and Industrial Engineering at The University of Texas at Austin and Academic Director of the Strategic Decision and Risk Management (SDRM). He also directs the Center for Engineering and Decision Analytics (CEDA) and the Engineering Management program.
His research interests include the theory and practice of decision analysis and its application to corporate strategy, public policy, and sports. His work has been featured in The Wall Street Journal, The New York Times, The Financial Times, and Sports Illustrated. In addition, Professor Bickel and his research are featured in the documentary Cool It. His research into climate engineering was named as the top approach to address climate change by a panel of economists, including three Nobel Laureates. He has also been a guest on the MLB Network show Clubhouse Confidential.
Professor Bickel joined Strategic Decisions Group in 1995, where he remains a director and partner. He has practiced decision analysis for nearly 25 years. He consults around the world in a range of industries, including oil and gas, electricity generation/transmission/delivery, energy trading and marketing, commodity and specialty chemicals, life sciences, financial services, and metals and mining.
He is an SDP Fellow and Past-President of the Decision Analysis Society.
Prof. Bickel holds both M.S. and Ph.D. degrees from the Department of Engineering-Economic Systems at Stanford University and a B.S. in mechanical engineering with a minor in economics from New Mexico State University.
Eric claims to be the only decision analyst listed in Hollywood's Internet Movie Database (imdb.me/jericbickel).
Carl Spetzler, a decision professional, has 40+ years of experience helping top management create innovative new strategies that deal with the complexities of uncertainty and risk over long time horizons. He is the CEO of management consulting firm Strategic Decisions Group and a Fellow and Past President of the Society of Decision Professionals. The author of Decision Quality: Value Creation from Better Business Decisions, he frequently leads senior executive retreats, briefings, and courses on decision making and risk management. His widely recognized for his contributions to the fields of decision science and decision quality