Evidence over hysteria - COVID-19
My original article on COVID-19 from March 20th, 2020 that was censored and canceled from Medium.
After watching the outbreak of COVID-19 for the past two months, I’ve followed the pace of the infection, its severity, and how our world is tackling the virus. While we should be concerned and diligent, the situation has dramatically elevated to a mob-like fear spreading faster than COVID-19 itself. When 13% of Americans believe they are currently infected with COVID-19 (mathematically impossible), full-on panic is blocking our ability to think clearly and determine how to deploy our resources to stop this virus. Over three-fourths of Americans are scared of what we are doing to our society through law and hysteria, not of infection or spreading COVID-19 to those most vulnerable.
The following article is a systematic overview of COVID-19 driven by data from medical professionals and academic articles that will help you understand what is going on (sources include CDC, WHO, NIH, NHS, University of Oxford, John Hopkins, Stanford, Harvard, NEJM, JAMA, and several others). I’m quite experienced at understanding virality, how things grow, and data. In my vocation, I’m most known for popularizing the “growth hacking movement” in Silicon Valley that specializes in driving rapid and viral adoption of technology products. Data is data. Our focus here isn’t treatments but numbers. You don’t need a special degree to understand what the data says and doesn’t say. Numbers are universal.
I hope you walk away with a more informed perspective on how you can help and fight back against the hysteria that is driving our country into a dark place. You can help us focus our scarce resources on those who are most vulnerable, who need our help.
Note: The following graphs and numbers are as of mid-March 2020. Things are moving quickly, so I update this article as much as possible. Most graphs are as of March 20th to 29th, 2020
Follow me on Twitter if you would like to see the updated graphics and articles.
Best,
Aaron Ginn
Total cases are the wrong metric
A critical question to ask yourself when you first look at a data set is, “What is our metric for success?”.
Let’s start at the top. How is it possible that more than 20% of Americans believe they will catch COVID-19? Here’s how. Vanity metrics — a single data point with no context.
Wouldn’t this picture scare you?
Look at all of those large red scary circles!
These images come from the now infamous John Hopkins COVID-19 tracking map. What started as a data transparency effort has now molded into an unintentional tool for hysteria and panic.
An important question to ask yourself is what do these bubbles actually mean? Each bubble represents the total number of COVID-19 cases per country. The situation looks serious, yet we know that this virus is over four months old, so how many of these cases are active?
Immediately, we now see that just under half of those terrifying red bubbles aren’t relevant or actionable. The total number of cases isn’t illustrative for what we should do now. This is a single vanity data point with no context; it isn’t information or knowledge. To know how to respond, we need more numbers to tell a story and to paint the full picture. As a metaphor, the daily revenue of a business doesn’t tell you a whole lot about profitability, capital structure, or overhead. The same goes for the total number of cases. The data isn’t actionable. We need to look at ratios and percentages to tell us what to do next — conversion rate, growth rate, and severity.
“The numbers are almost meaningless,” says Steve Goodman, a professor of epidemiology at Stanford University. He says there is a huge reservoir of people who have mild cases, and would not likely seek testing. According to Dr. Goodman, the rate of increase in positive results reflect a mixed-up combination of increased testing rates and spread of the virus. As well, positive tests in the US aren’t recording symptoms or demographic information. He argues that hospitalizations is a superior metric as it tracks more closely to managing resources and the data quality is superior.
Time lapsing new cases gives us perspective
Breaking down each country by the date of the first infection helps us track the growth and impact of the virus. We can see how total cases are growing against a consistent time scale.