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COVID-19 from a Decision Analysis Lens

Join us for our October chapter meeting! We are thrilled to present a 2 part session on COVID-19 from a Decision Analysis lens:
COVID-19 Forecasting, Three Cheers for Simple Models ~40mins
Using Value of Information (VOI) for COVID-19 Testing: Use of Bayes’ theorem to update/estimate the chance of having COVID given the results of your test ~10mins


Join ZoomMeeting

https://zoom.us/j/97571641528?pwd=aGJBaFRKT3Rzd05SSjFCTWZGSHNFQT09

Meeting ID:975 7164 1528
Passcode:631051
Location
https://zoom.us/j/97571641528?pwd=aGJBaFRKT3Rzd05SSjFCTWZGSHNFQT09
Country
CANADA
Dates
Oct 21, 2020
12:00 PM - 01:15 PM
Contact
Emilie Kamieniecki, SDP Calgary Chapter
Respondent Email

 

Part 1: COVID-19 Forecasting, Three Cheers for Simple Models ~40mins

Over the last six months we have witnessed policymakers grappling with how to respond to the spread of COVID-19across 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 fewCOVID-19 models do achieve this level of success, but most do not.

We will discuss this apparent paradox and the implications for decision analysis

Speaker: Eric Bickel, Professor, The University of Texas at Austin

Bio: Eric 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 JournalThe New York TimesThe 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).

 

Part 2: Using Value of Information (VOI) for COVID-19 Testing: Use of Bayes’ theorem to update/estimate the chance of having COVID given the results of your test

What would you do if you tested positive for COVID-19? Would you trust the test results if you’re asymptomatic? Would you seek immediate medical attention to prevent possible symptoms? Using a current COVID-19 testing example, we will discuss the concept of value of information (VOI). The concepts of VOI are part of the Decision Quality process. In this context we will explore using VOI and Bayes’ theorem to better inform a medical decision from a probabilistic perspective: What new information do we need to reduce key uncertainties surrounding COVID-19 test results?

Speaker: Kent Burkholder, VP of Decision Frameworks, outgoing SDP Calgary Chapter President

Bio: Kent is an advisor and consultant with broad experience in petroleum economics, decision and risk analysis framing, and economic modeling and implementation. He is highly skilled in decision facilitation and has developed and conducted many decision analysis, economic modeling, and value-of-information courses.

Kent's decision analysis application experience covers a wide range of topics including exploration, appraisal, development, business strategy, refining and new technology research. His extensive industry experience in the oil and gas sector allows him to provide high level support to executives and business decision-makers.

Mr. Burkholder holds a B.A.Sc. in Mechanical Engineering and is a member of the Society of Petroleum Engineers.

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Join ZoomMeeting

https://zoom.us/j/97571641528?pwd=aGJBaFRKT3Rzd05SSjFCTWZGSHNFQT09

Meeting ID:975 7164 1528

Passcode:631051


 

 

 


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