#30 What I learned from COVID-19 data visualization

Designers have tremendous power and responsibility to handle data and communicate the message visually through various media. The way two designers slice and present the data could make the stories in the two graphics look completely different. This hugely affects the perception and understanding of the viewers.

Today, we are flooded with all sorts of COVID-19 related information, visualized in various different ways. While it’s important to stay up to date with the latest information, I felt many visualizations that I see are often short-sighted, and lack a holistic perspective. Among the majority of these, I was able to find a few really good data visualizations that helped me understand a broader perspective. I’d like to share those with you.

Volume comparison with influenza

While the COVID-19 outbreak is terrifying, I strongly felt a need to calm down and view data objectively, so that I can take actions effectively and efficiently without reacting irrationally by fears. I also felt I have a responsibility as a designer to contribute to the world in this respect, which is why I decided to write this article.

Number of people infected to influenza annually vs. number of people infected to COVID-19 as of 4/2/2020 
Source:  US National Library of Medicine, Worldmeter
Number of people infected to influenza annually vs. number of people infected to COVID-19 as of 4/2/2020
Source: US National Library of Medicine, Worldmeter

The above chart shows there are up to 1 billion people who get infected with influenza every year, according to US National Library of Medicine. The latest data on the number of people infected to COVID-19 counts 1.7 million as of writing this today according to worldmeters.info. It’s still April, and the number is continuously growing at a rapid pace, so I don’t know how far it’s going to go.

On the other hand, I usually don’t pay much attention to the massive impact that influenza has every year. My typical attention on influenza has been nothing more than vaguely thinking “oh, it’s another flu season again…”.

This time, however, I paid more attention for the first time on how massive the effect of influenza has always been. Something that I probably never bothered to look at if COVID-19 never happened.

Fatality rate: Influenza vs. COVID-19 as of 4/2/2020
Source: US National Library of Medicine, Worldmeter
Fatality rate: Influenza vs. COVID-19 as of 4/2/2020
Source: US National Library of Medicine, Worldmeter

Fatality rates visualized with Y-axis manipulation

When I looked at the death count of both COVID-19 and influenza above, the fatality rate in COVID-19 was drastically higher than influenza. The break down by age of the fatality rates showed me a brutal reality that older age groups were significantly higher in COVID-19 compared to seasonal flu as seen in a chart below.

Source: OurWorldInData.org

But I noticed that the maximum value for Y-axis was set to 6%, mapped from the highest value which was the fatality rate of people with 60+ years for COVID-19. Since this graph shows percentages out of total, the maximum value should be set to 100% to give viewers the correct ratio of the data.

So I recreated a graph based on the same data, setting the maximum value of Y-axis to 100% (see below). It looked a lot different from the original chart. The magnitude of all the bars looked less threatening because the highest value was 6 out of 100%. This is how it should be rendered for a percentage-based graph in my opinion.

However, most of COVID-19 fatality rate charts that I see show the maximum value of Y-axis mapped to the highest value like the example above. This is not a data manipulation, but I think it ends up exaggerating the fatality rates of COVID-19 to be more frightening than needed. This article written by Ryan Mccready describes these tactics as one of the common ways to manipulate viewers.

OurWorldInData.org’s graph recreated with Y axis maximum value set to 100%

Top 10 cause of death worldwide

A bar graph showing top 10 cause of death in 2016 with COVID-19 death. Source: WHO, W
Source: WHO, Worldmeter

While looking into COVID-19 statistics, I also wanted to look into how many people die with what kind of causes annually and globally. According to WHO, a total of 56.9 million people died in 2016 worldwide. This number blew me away. In another words, we already live in a world where so many people die every year for various causes.

This is absolutely not to say that we don’t need to worry about COVID-19 by any means. But at the same time, I needed to understand the big picture from a global point of view. For example, 1.4 million people died from road injuries alone. Most of the time, I don’t even have a chance to see such global statistics. COVID-19 pandemic gave me an opportunity to consider all these numbers around deaths in a global context. It also made me appreciate the fact that I still live healthily today.

History of pandemics

I came across a stunning data visualization of the history of pandemics created by visualcapitalist.com below. This introduced me to a history of pandemics. It gave me a broader perspective that humans have always lived in this continuum of constant pandemics. Below is a quote from visualcapitalist.com.

THROUGHOUT HISTORY, as humans spread across the world, infectious diseases have been a constant companion. Even in this modern era, outbreaks are nearly constant.

A diagram "History of Pandemics" 
 with pandemics visualized in virus-like spheres. Source: Visualcapitalist.com
Source: Visualcapitalist.com

While this diagram was visually intriguing and the content presented was very good and informative, I found a few issues as stated below.

  • Abstracted and generalized virus-like representation was visually interesting and drew my attention in the first place, but it was a more or less cosmetic effect.
  • The three-dimensional depth of field towards Z-axis made it impossible to visually compare the sizes of virus-like spheres from different eras.
  • It was unclear how the values were mapped to the sizes of the spheres. Was it radius, or the mass? If mapped to the mass, it was not visually intuitive.
  • Color coding seemed arbitrary.
  • Spherical form factor made it hard to plot precisely on a timeline.
  • Because visual size comparison along the timeline was not possible, the designer duplicated another set of spheres just for the size comparison.

Different 3D visualization can make data easier to compare

Below is how I recreated the data visualization, which allowed a viewer to:

  1. See a precise plotting of each pandemic on the timeline
  2. Compare size of pandemics visually

Obviously it no longer had the colorful virus-like spheres that the original diagram had which drew my attention in the first place. Instead, those were replaced by a bunch of boring bars in a 3D space!

But it achieved successfully in presenting above two aspects without having to duplicate another set of data.

As you can see, a devastating impact and magnitude of Black Death was massive, which was missing in the original diagram as it was far back along the z-axis timeline thus rendered much smaller.

History of pandemics visualized differently for easier comparison - 3D bar graph, created by the author.
Data is based on Visualcapitalist.com and Worldmeter
History of pandemics visualized differently for easier comparison – 3D bar graph
Data is based on Visualcapitalist.com and Worldmeter

Interestingly, this 3D visualization allowed an overall holistic view of the data along the timeline while each data still being relatively comparable, as opposed to a 2D bar chart below which became a mess.

In the 2D bar chart, 1800–2000 area was too narrow to fit in all the pandemics that concentrated in that time period. As a result, too many callout lines were overlaid on top of each other and the bars, making it hard to track and read which one was which.

When it came to the most recent 5 pandemics, it was impossible to show on the same plane, which forced those to be separated in a callout box on the top right corner.

History of pandemics visualized differently in a simple 2D bar graph, which ended up a mess, experimented by the author.
Data is based on Visualcapitalist.com and Worldmeter
History of pandemics visualized differently in a simple 2D bar graph, which ended up a mess.
Data is based on 
Visualcapitalist.com and Worldmeter

Cause and effect of social distancing brilliantly visualized

Here’s another chart that I found extremely powerful and convincing in showing the effectiveness of social distancing, and the consequence of abandoning it too early. I found this in Vox’s article titled How we know ending social distancing will lead to more deaths, in one chart.

The highest peak comes after social distancing measures were lifted, with the death rate falling only after they were reinstituted.

Source: Vox
A graph that shows the effectiveness of social distancing from St Louis case. Source: Vox
Source: Vox

From visual design perspective, the chart above is nothing special. Rather, it’s a very basic black and white line graph accompanied by bars on the bottom.

However, the content that it carries has significant value in my opinion. This is a clear evidence of how social distancing was effective in a pandemic situation. The way the death rate line graph was combined with the timeline of school closure and the public gathering ban was a brilliant execution to highlight a clear cause and effect relationship between those elements visually. I would say this is a masterpiece of infographics.

Instead of constantly showing fatality rates in Y-axis-manipulated graphs, a quality chart like this should be shared and circulated more across our news and social media. That would be far more informative, helpful and effective for viewers to understand the criticality of social distancing because it clearly illustrates its effectiveness without further explanation. That’s the power of data visualization.

Understanding a big picture is an important first step to take actions

Confronting with loads of COVID-19 data visualizations while sheltering in place gave me an ironically interesting opportunity to think through these as a designer and it motivated me to write this article. It made me realize that asking good questions and understanding the big picture is crucial in order to grasp the situation unbiased, so that I can take the correct actions with confidence.

  • Can I get a big picture of past pandemics in our history?
  • Do I know the impact of influenza every year in volume?
  • How many people die every year worldwide for what causes?
  • Why is social distancing important?
  • What can I do to help myself, my family, and others?

The whole situation reminded me of Rachel Carson’s quote from her famous book, Silent Spring, which I borrowed in a Global Village animation that I wrote about in my previous article.

The human race is challenged more than ever before to demonstrate our mastery, not over nature but of ourselves.

Rachel Carson, Silent Spring 1962

I feel like we as the human race are challenged to not only care about ourselves but also care for others. To demonstrate our mastery, understanding is one of the first important steps. I took this step, and now I feel more prepared than before.

This article was published on Medium in Nightingale, the Journal of the Data Visualization Society, as a collaboration article with Nightingale’s editor and healthcare data visualization expert, Amanda Makulec, and Jason Forrest, Nightingale’s editor in chief.

If you are interested in data visualization, check out the following articles too!

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