COVID-19 Pandemic Resources
The board and staff of the American Statistical Association have compiled a few on-demand sources that explain concepts critical to understanding and evaluating the scientific information available to the public.
Understanding Graphs and Data Visualizations
Title: Four Things to Know to Understand a Commonly Used COVID-19 Data Visualization
Date: April 28, 2020
Author: Produced and animated by Joss Fong
About the Author/Source: Vox is a highly respected media outlet. Their sources are provided at the end of the video.
Length: About 5 minutes
Synopsis: This highly visual and especially clear video explains a graph frequently used to compare the growth rate of COVID-19 cases.
Title: How to Spot a Misleading Graph
Date: July 6, 2017
Author: Lea Gaslowitz
About the Author/Source: Lea Gaslowitz is the founder of Front Porch Math, which creates video math lessons for high-school students. TED-Ed is part of the TED family. See https://ed.ted.com/about.
Length: About 4 minutes
Synopsis: A good graph can help us quickly understand important information, but poor graphs confuse. This video provides a basic, clear, amusing illustrated explanation of common errors in graphs.
Title: Five Questions and Terminology Used in Infectious Disease Epidemiology
Source: The Center for Communicable Disease Dynamics (CCDD) at the Harvard School of Public Health
Date: January 28, 2020
Length: About 30 tweets
Synopsis: How do we measure the spread of a disease? And what the heck is R0? This tweetorial (using a series of tweets to explain a topic) addresses five questions and terminology used in infectious disease epidemiology.
Title: COVID-19 Data Dives: The Takeaways from Seroprevalence Surveys
Source: Medscape Internal Medicine (requires free registration to access)
Date: May 4, 2020
Author: Natalie Dean
About the Author/Source: Natalie Dean is a statistician with expertise in infectious disease epidemiology. Medscape is a respected online news source for physicians and researchers.
Length: About 450 words
Synopsis: How do we know how many people have been infected with a disease? Through serology studies. This brief article explains what serology studies are and how they should be designed.
Title: What Happens Next? COVID-19 Futures, Explained with Playable Simulations
Source: Blog post
Date: May 1, 2020
Authors: Marcel Salathé (epidemiologist) and Nicky Case (art/code)
About the Authors: Marcel Salathé is associate professor of epidemiology at EPFL. Nicky Case designs instructional materials to help people understand complex algorithms.
Length: About 30 minutes to read and play all the simulations
Synopsis: What’s going on with the spread of a disease like COVID-19? The authors explain basic concepts of epidemiology and modeling, using interactive simulations to illustrate these concepts.
Title: Exponential Growth and Epidemiology
Date: March 8, 2020
Author: Grant Sanderson
About the Author/Source: 3blue1brown is a YouTube channel that uses visualization to explain mathematical concepts.
Length: About 9 minutes
Synopsis: We hear about exponential growth frequently, but what does it mean? Here is a highly visual explanation of what it means and why it happens.
Title: Introduction to an Infectious Disease Model (Part 1)
Source: Author post on YouTube
Date: January 10, 2013
Author: Duane Nykamp, associate professor in the University of Minnesota School of Mathematics
Length: About 11 minutes
Synopsis: If you want a simple visualization of disease models, this is a good place to start.
Title: What Are COVID-19 Models Modeling?
Source: The Society Pages (TSP) is an open-access social science project headquartered in the University of Minnesota Department of Sociology and supported by individual donors. TSP consists of in-house “TSP HQ” articles, blogs, and podcasts; community pages; and content produced by its partners.
Date: April 8, 2020
Author: Jimi Adams is an associate professor in the University of Colorado Denver Department of Health and Behavioral Sciences and affiliate faculty at the Institute of Behavioral Science at CU Boulder.
About the Author/Source: Although not a statistician or epidemiologist (his PhD is in sociology), Jimi Adams researches social network models and can speak about the types, goals, and uncertainties in population-based models.
Length: About 2,800 words
Synopsis: Models, models, models! We hear about them all the time. But what are they for? This non-technical article explains the aims of models (explain, predict, forecast), what they produce, and how they work.
Title: What Is ‘The Curve’? Making Sense of COVID-19 Models
Source: PATH is a global team working to accelerate health equity. It advises and partners with public institutions, businesses, grassroots groups, and investors to solve the world’s health challenges.
Date: April 7, 2020
Authors: Anna Volbrecht, PATH senior communications associate, and Hannah Slater, CRISIS campaign manager
About the Authors/Source: The authors are communications associates.
Length: About 900 words
Synopsis: We hear we want to flatten the curve, but what is the curve? This easy-to-read article covers basic models and looks at what is needed to improve them and apply them in various geographical settings where demographic factors differ.
Title: Why It’s So Freaking Hard to Make a Good COVID-19 Model
Date: March 31, 2020
Authors: Maggie Koerth, Laura Bronner, and Jasmine Mithani
About the Authors/Source: FiveThirtyEight, founded by Nate Silver, is a respected source for data journalism.
Length: About 2,900 words
Synopsis: We read about models for the pandemic, but the models give different results. The authors provide well-written and illustrated explanations of how models are made and why results can vary so much.
Additional: Here is a 36-minute video involving these authors that further explains the written article.
Title: Why We Randomize
Source: University College Cork School of Public Health
Date: March 27, 2020
Author: Darren Dahly
About the Author/Source: Darren Dahly is an epidemiologist in the University College Cork School of Public Health.
Length: 6 minutes
Synopsis: Using clever cartoon illustrations, this video explains randomization, a fundamental part of good design of medical experiments. If you want to understand why we can’t just give sick people the medicine and know the medicine works if they get better, here’s a video for you.
Titles: Sensitivity and Specificity; Positive Predictive Value
Dates: September 16, 2016; March 12, 2019
About the Source: Medmastery provides online instruction to improve clinical practice.
Length: Two videos totaling about 10 minutes
Synopsis: If you have tested positive for a disease, does that mean you have it? It does not! So, what is the probability you actually do have the disease? These two videos explain how to understand this so you can ask the right questions.
Title: Risk and How to Use a Risk Matrix
Source: Let’s Learn Public Health
Date: June 8, 2018
Author: Ranil Appuhamy
About the Author/Source: Ranil Appuhamy is a medical doctor with a master’s degree in international public health. Let’s Learn Public Health is a YouTube channel he created to explain basic concepts of public health.
Length: About 5 minutes
Synopsis: An animated video provides a basic introduction to risk and the concept of a risk matrix.
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*The resources posted on this site include links to information created by public and private organizations and individuals. These links are provided to support public education. The ASA cannot guarantee the accuracy or completeness of this information. The inclusion of these links is not intended to endorse views expressed, or products or services offered, on these non-ASA sites.