Presented By: U-M Industrial & Operations Engineering
SEMINAR: "COVID-19 Forecasting: Three Cheers for Simple Models" — Eric Bickel
The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
Title:
COVID-19 Forecasting: Three Cheers for Simple Models
Abstract:
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 in detail. 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 (one-parameter) model that we have used to forecast the spread of COVID-19 at the national, state, and local level. Surely large models with dozens of parameters, backed by a team of experts, should outperform a simple model that has one input and runs in Excel. As we discuss, a few COVID-19 models do achieve this level of success.
We will discuss this apparent paradox and the implications for decision analysis.
Bio:
Eric Bickel is a professor and director of both the Operations Research & Industrial Engineering and Engineering Management programs at The University of Texas at Austin. Eric holds a courtesy appointment in the Department of Information, Risk, and Operations Management in the McCombs School of Business and directs the Center for Engineering and Decision Analytics (CEDA).
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.
Eric joined Strategic Decisions Group in 1995, where he remains a director and partner. He has practiced decision analysis for 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 Past-President of the Decision Analysis Society.
Eric 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).
Title:
COVID-19 Forecasting: Three Cheers for Simple Models
Abstract:
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 in detail. 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 (one-parameter) model that we have used to forecast the spread of COVID-19 at the national, state, and local level. Surely large models with dozens of parameters, backed by a team of experts, should outperform a simple model that has one input and runs in Excel. As we discuss, a few COVID-19 models do achieve this level of success.
We will discuss this apparent paradox and the implications for decision analysis.
Bio:
Eric Bickel is a professor and director of both the Operations Research & Industrial Engineering and Engineering Management programs at The University of Texas at Austin. Eric holds a courtesy appointment in the Department of Information, Risk, and Operations Management in the McCombs School of Business and directs the Center for Engineering and Decision Analytics (CEDA).
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.
Eric joined Strategic Decisions Group in 1995, where he remains a director and partner. He has practiced decision analysis for 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 Past-President of the Decision Analysis Society.
Eric 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).
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