Dr. Page has started to publish a video series Understanding Epidemics “I am working to produce a collection of videos explaining how models and data can help us understand the COVID epidemic and make policy”
UNDERSTANDING EPIDEMICS VIDEO #5 - THE IHME MODEL AND CURVE FITTING
This fifth video in the understanding epidemic series describes curve fitting models and the IHME model that has been used to predict peak ICU and hospital demand. It shows how the model complements the SIR model, network model, and fatality rate model. On why we need many models and the limitations of this one "what we need are models that are more computational and have micro-level foundations at its' core" The IHME Model and Curve Fitting
UNDERSTANDING EPIDEMICS VIDEO #4 - WHY NETWORKS MATTER IN MODELS OF EPIDEMICS: Scott E Page and Abigail Jacobs
In this video Dr. Page and colleague Abigail Jacobs, Professor of Complex Systems and the School of Information, talk about networks and social structure and they impact disease transmission; types of networks we interact with and the effect on spread; so called 'superspreaders'; an SEIR model; contract tracing and more. "The second order effects are really important when we think about who we are interacting with". View Why Networks Matter in Models of Epidemics
UNDERSTANDING EPIDEMICS VIDEO #3 - THE SIR MODEL AND COVID-19
This video explains the SIR model, a core model of epidemiology. SIR stands for Suseptible-Infected-Recovered “In this video, I describe the SIR model of epidemics, how it produces exponential growth, and how it reveals a "tipping point" in whether diseases spread based on their reproduction number. I discuss the model and its implications in the context of COVID-19” View The SIR Model and COVID-19 Understanding Epidemics - A Many Model Approach
UNDERSTANDING EPIDEMICS VIDEO #2 - FATALITY RATE MODELS
In this video, Dr. Page describes fatality rate models, categories, and the bias-variance tradeoff. "Why do we model? We model to reason, explain, design, communicate, act, predict, and explain ... We describe four classes of models for epidemics: Expected Fatality Models, Curve Fitting Models, Mathematical Models - SIR, and Agent Based Models." View ‘Fatality Rate Models’ Understanding Epidemics - A Many Model Approach
UNDERSTANDING EPIDEMICS VIDEO #1 - EXPLAINING FATALITY RATES IN AN EPIDEMIC
“To help clarify a common misunderstanding about the fatality rate of COVID-19, I made a short educational video….Newspapers have been publishing "running average" fatality rates for COVID-19. These averages are far less than 1%. In this video (only 5 minutes and no math) I explain why that could understate the true fatality rate 8 fold (that of course, depends on the data being correct and cases may be understated.) Data issues aside, the core argument of the video holds." View: Explaining Fatality Rates in an Epidemic
Dr. Page and friend Emile Servan-Schreiber (Hypermind.com) are in the middle of an active learning experiment that was available free to individuals, instructors and student. They together an online prediction market focusing on eight questions related to the pandemic. The market opened in March with several hundred taking part. It on April 15.
“We built this so people can use the market as an active learning exercise as our classes go online. We have set up the site so that you can give a sponsor code and track only your students."
The questions are fun and cover everything from the DJIA to Taylor Swift and the Hedonometer.
See here for details. We look forward to finding out how it turned out!
A WEBSITE DEDICATED TO HOW MODELS MATTER FOR COVID-19
Scott Page John Seely Brown Distinguished University Professor of Complexity, Social Science, and Management is currently teaching Management Organization 410 - Collective Intelligence. With the students in this class - they put together a WEBSITE that details how models matter for COVID-19
From the website:
This site is a collaborative class project of MO 410 Collective Intelligence from University of Michigan.
Our goals are four-fold:
1) To demonstrate how models are helping to explain and predict the spread of the COVID-19 pandemic, how they enable clearer communication and how they are helping to design better policies.
2) To show how ensembles of models and diverse models lead to more robust understanding and policies.
3) To explain in clear language the key models.
4) To use our collective energies and intelligence to produce public knowledge.