Over the past 15 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 webinar, 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. Others 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. This model could serve as a forecasting benchmark.
Eric Bickel is Professor and Director of the Graduate Program in Operations Research & Industrial Engineering at The University of Texas at Austin. His research interests include the theory and practice of decision making under uncertainty and its application to business strategy and public policy. He is a partner and member of the board of management consulting firm Strategic Decisions Group.
Carl Spetzler is an author, speaker, and consultant with more than 40 years of experience working with top management and boards to make value-creating strategic decisions in the face of uncertainty. He is a co-author of Decision Quality: Value Creation from Better Business Decisions (Wiley, 2016). Carl is chairman of management consulting firm Strategic Decisions Group.