Why COVID-19 Data Confuses People
By: Matthew Raphaelson
Tags: Storytelling, Data Science, Decision Making, Behavioral Economics, Covid 19

A two-dimensional table like this one can be read two ways: horizontally (where the percentages add up to 100% across the rows); and vertically (where the percentages add up to 100% down the columns).

The horizontal view corresponds to the first headline. It’s the view of politicians and professionals, who are looking across society. Crucially, it’s much easier to collect data in this view — simply count the people in the hospital. The data organized this way is useful for resource and capacity planning.

The vertical view corresponds to the second headline. It’s the view of the general population, including most of the media. It answers the question “What does this mean for me?” Unfortunately, data is harder to collect for the vertical view, and rarely presented this way.

There you have it — same information, two accurate but different views and interpretations. The simple reason that younger adults can make up a large percentage of hospitalizations while having a low hospitalization rate? There are so many more young adults than old people.

We need this critical piece of “prior information” on the overall population to reconcile the two views, yet it is too often omitted — a form of what Daniel Kahneman (and others) call “Base Rate Neglect.” [3]

Mixed-up Views, Mixed-up People

Now that we understand that both views are correct and each answers different but important questions, does that mean there isn’t a problem?

No, there’s a BIG problem: Most of the time the data is presented to us in the horizontal view, while most of us are interested in the vertical view question: “What does it mean for me?” [4]

Mixed up views result in mixed up people. Many simply tune out. Others reach wrong conclusions — in this case, the alarming-but-wrong conclusion that over 40% of younger adults with COVID-19 are being hospitalized. “Mixed messages allow people to follow their biases and believe whatever they want.” [5]

Even worse: when the “expert” (horizontal) view and the “popular” (vertical) view are left unreconciled, it can breed mistrust of experts and their motives. Accuracy without clarity may lead to the boomerang effect, “the unintended consequence of an attempt to persuade resulting in the adoption of the opposite position instead.” [6].

In the early stages of COVID-19 pandemic, U.S. Surgeon General Dr. Jerome Adams asked Americans to stop buying masks because “They are NOT effective in preventing the general public from catching #Coronavirus, but if healthcare providers can’t get them to care for sick patients, it puts them and our communities at risk.” He was legitimately worried about the health care system (horizontal view) but blurred the risk to the public (vertical view). [7]

Yet many people were aware of how Asian countries treated masks as essential to personal safety, causing confusion and sowing doubts about America’s experts and their motives. Sadly, some people have responded in opposite to what the experts intended — at first by hoarding masks, and later (after the experts reversed course) by refusing to wear them.

Accuracy and Clarity for Data Scientists

Recognizing that there are two views provides an opportunity for the data scientist to lead in the quest for accuracy and clarity. We mentioned earlier that it’s often difficult to compile the population data for the vertical view. This is especially true when we’re dividing the population into sub-groups.

Suppose the COVID-19 hospitalization data was missing much of the data for the vertical view: