This is a multi-chapter post! Read the other chapters here:

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

In this final chapter, we’ll dive deeper into some of the insightful stories which have been published about the novel coronavirus and the COVID-19 pandemic. Rather than looking at single charts, we’ll highlight some long-form stories about the origin of the virus, how it works, and how it spread.

Two concepts should be explained beforehand: visual storytelling, and scrollytelling. Lorenzo Amabili explains both of them very well in his Nightingale article ‘From Storytelling To Scrollytelling: A Short Introduction and Beyond‘ (unfortunately a premium article):

• Visual storytelling, also called narrative visualisation, consists of creating a logical sequence of related (data-driven) visualisations, or visual elements, needed to convey a message to an audience in an engaging and effective way.

In other words, different visuals are strung together to form a logical story, which will keep readers more engaged and increase their understanding. It also allows you to discuss more complex topics by breaking them down into multiple easily understood visuals. These visuals will often be data-driven, but they can also be illustrations, videos, maps,… the possibilities are virtually endless!

• Scrollytelling is visual storytelling for the web: a powerful technique based on a simple concept: new content and visuals appear or change through transitions as users scroll down or up the web page.

This way of presenting information keeps the reader actively engaged with the content and the story. Furthermore, people can easily control the pace by scrolling up and down, back and forth through the story if they choose to re-read difficult sections or skip certain less interesting parts.

I must admit, I absolutely LOVE ❤ scrollytelling, and particularly well-executed examples are appearing more and more regularly in journalism. These advances are fueled by more innovative newsrooms such as the New York Times or the Washington Post, but quickly spreading throughout the world. For example, in Belgium, De Tijd regularly brings engaging scrollytelling stories by the team of Thomas Roelens and, before that, Maarten Lambrechts.

The remainder of this text is dedicated to some remarkable and marvelous examples of visual storytelling and scrollytelling about the coronavirus pandemic. This is absolutely not intended to be an exhaustive list, but rather a collection of interesting examples I encountered during my background research for these blog posts.

In March, The Economist published ‘Understanding SARS-CoV-2 and the drugs that might lessen its power‘, an in-depth story about the science behind the virus: how is SARS-CoV-2 built, how does it work and replicate itself, what does its genetic sequence look like, and which drugs might have a serious shot at eliminating it. A pretty hardcore scientific explanation, but made easily digestible thanks to some very clear and beautiful illustrations, such as this one by infographer Manuel Bortoletti:

Several scrollytelling articles have been created to explain the reasoning behind the epidemiological curve, how we can flatten it, and differences between different countries’ strategies to do so.

• ‘What Happens Next?‘ by epidemiologist Marcel Salathé and coder/designer Nicky Case explains the curve, SIR and SEIR models using playable simulations.
• ‘What we can learn from the countries winning the coronavirus fight‘ by ABC News uses scrollytelling to explain in detail what exponential growth is, how logarithmic charts work, and what flattening the curve really means.
• At this point, it might be useful to bring up again the infamous Washington Post article ‘Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”‘ by Harry Stevens, which we discussed in chapter 4, and is a great example of visual storytelling in itself.
• Another Washington Post article worth mentioning, on a similar note, is ‘How epidemics like covid-19 end (and how to end them faster)‘, also showing the effectiveness of wearing facemasks, or enforcing quarantaine.

Detail from ‘How epidemics like covid-19 end (and how to end them faster)’ (Washington Post)

• Or, if you prefer the Spanish version, ‘Cómo el confinamiento frena la cadena de contagios‘ by El País.

‘How the Virus Got Out‘ by the New York Times uses mesmerizing animations and crystal-clear maps to show how the virus spread from Wuhan, through long-distance train and air travel, to other parts of China and other countries throughout the world. They end with Donald Trump’s quote ‘The virus will not have a chance against us’ on March 11, placed next to a visual showing how by then, the virus already had a secure foothold in the US, with just over 1000 cases.

Detail from ‘How the Virus Got Out’ (New York Times)

This detailed look at how the virus spreads is taken one step further by Reuters Graphics in ‘The Korean Clusters‘. Thanks to insanely accurate contact tracing data being gathered in South Korea, the authorities were able to identify each individual case and how they infected each other. During the first four weeks, the disease was relatively contained, but then ‘patient 31’ emerged, who had at least 1160 traced contacts and went to crowded places such as churches and a hotel buffet while being infected. This is by far one of the most captivating visual stories I have encountered over the past few months!

Detail from ‘The Korean Clusters’ (Reuters Graphics), an amazing piece of visual storytelling.

Detail from ‘How Bad Will the Coronavirus Outbreak Get?’ (New York Times)

Detail from ‘The Workers Who Face the Greatest Coronavirus Risk’ (New York Times). Did I already mention that I ❤ scrollytelling stories like this?

This is a multi-chapter blog post!

Continue reading:

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

For all your comments, suggestions, errors, links and additional information, you can contact me at koen@baryon.be or via Twitter at @koen_vde.

Disclaimer: I am not a medical doctor or a virologist. I am a physicist running my own business (Baryon) focused on information design.

This is a multi-chapter post!

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

There’s so much coronavirus data out there – and more is being generated every second. In order to keep the overview, or at least keep some of our sanity, many organizations and individuals have created dashboards. Rather than being a lengthy, descriptive overview, this chapter will point you to some of the best dashboards out there. For those who want to stay on top of things, even during these crazy times.

The WHO maintains a Situation Dashboard. This one was recently updated to a much brighter and cleaner design. Make sure to scroll down to see more than just the map! Especially the breakdowns by country are a great addition:

Country breakdowns for the number of cases on the WHO Situation Dashboard.

The WHO Regional Office for Europe also maintains a dashboard, albeit of somewhat dubious design quality. Nevertheless, it contains some interesting gauge graphs (created with Infogram) to illustrate how much the most affected countries contribute to the total number of cases in Europe:

Gauge graphs indicating the contribution of the most affected countries to the total number of cases and deaths (Source: WHO Regional Office for Europe).

The European CDC (Centre for Disease Prevention and Control) has its own dashboard, although in my experience, it is rather difficult to navigate, with a complex layout, a few visual bugs and some counterintuitive (sideways) scrolling which makes it difficult to quickly find the numbers you are looking for.

Cumulative confirmed cases in Europe as visualized on the JHU dashboard.

General overview of the JHU dashboard.

More small multiples! MOAR! (Source: Financial Times)

Treemap and strip chart on the Bing COVID-19 dashboard.

This is a multi-chapter blog post!

Continue reading:

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

For all your comments, suggestions, errors, links and additional information, you can contact me at koen@baryon.be or via Twitter at @koen_vde.

Disclaimer: I am not a medical doctor or a virologist. I am a physicist running my own business (Baryon) focused on information design.

This is a multi-chapter post!

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

Data visualizations rarely become hugely popular among the general public. But recently, a visual appeared which quickly spread on social media, in newspapers and magazines, and even in press conferences and on television. Hundreds of millions have probably seen it by now. Yes, the Flattening the Curve visual is truly a remarkable success story in the dataviz world. So let’s dive into it!

Let’s start by looking at a prime example of a ‘flatten the curve’ visual, the one in the COVID-19 #Coronavirus Data Pack at Information is Beautiful, as it combines a simple and clean curve design with a lot of additional information:

Flattening the curve visual from the Information is Beautiful COVID-19 #Coronavirus Data Pack.

We are looking at how the expected number of daily infections will evolve over time since the date of the first case. As described in chapter 2, the shape of these curves is expected to be more or less Gaussian in nature, with an exponential growth phase at the start of the outbreak and reaching a peak at a certain point in time, typically several weeks since the date of the first case. The crux of the graph is the dotted line – the capacity of our healthcare system. There is only a certain number of patients we can treat simultaneously in an effective way, be it due to a lack of equipment (intensive care beds, ventilators,…) or a lack of personnel.

The color choice is very smart here. Orange is related to the curve we want to avoid: an outbreak without protective measures being taken. In this case the number of daily infections will quickly outpace the capacity of our healthcare system, leading to a high fatality rate because patients cannot all be treated effectively.

Blue indicates the scenario we aim for when protective measures are taken (the protective measures are also listed in the figure above). In this case, the spread of the disease will be slowed down. This implies that the outbreak might take longer to die down, but more importantly, the peak in the number of daily infections will not (or not by much) exceed the capacity of our healthcare system. This enables us to treat all patients as effectively as possible and lower the fatality rate.

In one sentence: we need to take protective measures to flatten the curve.

Many visualizers have come up with their own version of the ‘flatten the curve’ graphic. One of the more popular examples was the following animation by illustrator Toby Morris and microbiologist Siouxsie Wiles for The Spinoff, an online magazine from New Zealand:

Flattening the curve visual by illustrator Toby Morris and microbiologist Siouxsie Wiles for The Spinoff.

Before we dive into the many different variations of this curve, we need to briefly discuss a more technical point: should both graphs have the same area, or not?

When looking at both visuals above, this is not very clear. In the Information is Beautiful version, it might seem that the blue, flat curve has a somewhat smaller surface area compared to the orange version, but it is not very extreme. In the animated version, both appear to have a similar area. In some visuals, the difference is much more extreme, such as in this one by Vox:

Flattening the curve visual by Vox on March 10, 2020.

‘Curve 0’: the original Flattening the Curve visual as it appeared in the CDC report ‘Community Mitigation Guidelines to Prevent Pandemic Influenza‘ in 2017.

Key visual from Tomas Pueyo’s article ‘The Hammer and the Dance‘ on how to use protective measures to fight the epidemic.

Probably the first appearance of the Flattening the Curve visual in the media. (The Economist)

Thanks for sharing your wisdom, Drew. (Source)

This is a multi-chapter post!

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

A pandemic has a strong geographical factor attached to it, so obviously we are drawn to using maps to visualize how the virus is spreading. Both data visualizers and their audience simply love maps, and I personally do to. As a child, my (geographical, historical, biological, even biblical) atlases where my favourite books and I could browse through them for days. However, pretty as they may be, maps have their own pitfalls and caveats. So be prepared!

Let’s start with some of the most well-designed examples of maps I have encountered during my research for this chapter. The absolute winner, in my opinion, are these clean but very effective maps by the Washington Post:

Map showing the global spread of the coronavirus on March 27, 2020 (Washington Post).

To further clarify things, these maps are complemented by a simple table detailing the exact number of confirmed infections or deaths. This gives the reader the choice to look at the broader picture, dive into the detailed numbers, or both.

Table showing the global spread of the coronavirus on March 27, 2020 (Washington Post).

It should be noted that the BBC uses very similar, equally beautiful maps. These are examples of proportional symbol maps, or what most normal people simply call bubble maps. But why exactly do these bubble maps work so well?

Xkcd cartoon 1138

This is a multi-chapter post!

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

Let me warn you in advance: this will probably be the most theoretical and mathematical chapter of this entire blog post. We’ll have a short look at the underlying scientific principles of a pandemic and analyze how this translates to visualizing data. If that’s not really your thing, it’s totally okay to just look at the pictures and then skip to the next chapter! 😉

A pandemic disease is a complex thing to model. We live in a world of nearly 8 billion people in over 230 countries, connected with each other through 100.000 daily plane flights and an equally mind-boggling number of train and bus rides. Nevertheless, many experts have attempted to model the spread of a disease in a closely-connected world.

For example, professor biostatistics Kurt Barbé models the pandemic spread through a first order differential equation, resulting in the number of active infections following a Gaussian curve, with its typical bell shaped profile:

Different scenarios for the coronavirus spread in Belgium, as modeled by professor Kurt Barbé (March 18, 2020).

This is a simplified but not unrealistic model. For example, if we look at the number of active COVID-19 cases in China, where the peak has nearly passed, we can see that the Gaussian profile is a pretty good approximation (the sudden increase in the number of cases on February 13 is the result of a change in reporting methodology).

Evolution of the number of active SARS-CoV-2 infections in China.

We can assume that if the number of active infections follows a Gaussian-shaped profile, the number of new infections and the number of deaths will also follow Gaussian profiles. If we plot cumulative data, such as the total number of confirmed cases or the total number of deaths, this will follow an S-shaped cumulative function profile — the integral of the Gaussian function. For example, the total number of deaths by COVID-19 in China looks like this:

Evolution of the total number of COVID-19-related deaths in China.

Financial Times graphic by John Burn-Murdoch on the growth of the number of coronavirus cases in different countries.

Oh no! Not another coronavirus post! Yes, I know, we are bombarded by pandemic content these days. My apologies for creating even more. However, it is not my purpose to bore you with more of the same, or to confuse you with pointless details. Being a passionate information designer I decided to have a look at good and bad practices in COVID-19 related content from a data visualization point of view. I hope this will be a useful and inspiring overview.

This is a multi-chapter post!

• Chapter 1: Finding reliable data
• Chapter 2: Visualizing exponential growth
  
• Chapter 3: Mapping the virus
• Chapter 4: We need to talk about flattening the curve
• Chapter 5: On top of the outbreak with daunting dashboards
• Chapter 6: Viral scrollytelling

We are living in remarkable times. The novel coronavirus is causing an epidemic spreading with a velocity we have never experienced before. Busy long-distance air and rail traffic have made it impossible to contain the virus after its first outbreak in China. For the first time our modern world is confronted with a pandemic of this scale and magnitude, and our healthcare systems are being put to the test.

But in fighting these challenges, the world has never been as united as today. Research teams across the globe are working together to develop cures, social media are used extensively to keep everyone informed, and innovative companies are coming up with solutions to keep people at home and the virus at bay. Technology plays a crucial role in this fight.

As an information designer, I am specifically fascinated by the efforts of the data science and visualization communities. The newest developments in these fields are put to use to turn a complex and rapidly changing topic into easy-to-communicate visuals. In only a matter of days, nearly everyone is familiar with the ‘flatten the curve’ visuals, or Washington Post’s animations on the impact of social distancing.

In this post, we will explore some of the marvelous ways people around the world are using data visualization in the fight against the novel coronavirus.

As noted by Edward Tufte, excellent graphics consist of complex ideas communicated with clarity, precision, and efficiency. At the core of a good data visual, therefore, lies accurate data. So before we start diving into coronavirus graphs, we will first take a brief stop at trustworthy data sources.

There are currently three important places where one can obtain reliable and relatively complete aggregate data about the Coronavirus epidemic:

• World Health Organization
  The World Health Organization publishes daily Situation reports detailing the number of confirmed cases and deaths per country. They also provide a Situation dashboard which is updated three times per day.

WHO Novel Coronavirus Situation Dashboard

• John Hopkins University
  Researchers at John Hopkins University also maintain a dashboard providing an overview of the current number of cases, deaths and recoveries on a per country basis. The underlying data is made freely available through GitHub.

John Hopkins University Coronavirus Dashboard

• European Center for Disease Control and Prevention
  The ECDC publishes daily statistics on the pandemic for the entire country (despite its name!). Data is published daily at 1 p.m. CET and is presented on a situation update page.

• Our World in Data
  The team of Max Roser collects and combines all available data and information about the epidemic on a single page. This excellent summary provides interactive charts on many different topics ranging from the number of cases to symptoms, incubation period and fatality rate. Each chart comes with a downloadable data set.

Collecting and aggregating global data in a rapidly changing environment, such as during a pandemic, is obviously very tricky. None of the above datasets should therefore be considered an ‘absolute truth’, as minor errors are bound to happen. Such errors can be related to reporting difficulties or contradicting sources, or differences and shifts in methodology, but can also be due to minor errors such as typos.

As an example, let us compare the three datasets above for the total number of confirmed cases in Belgium (between March 1 and March 19) with the official numbers communicated by the Belgian government (which can be found here).

Comparison between different data sources of the reported total number of confirmed COVID-19 cases in Belgium between March 1 and March 19, 2020.

Immediately we can note some discrepancies. The John Hopkins University data follows the government data most closely, with an exception on March 12 where for some reason the number was not updated.

The two other datasets (WHO and Our World in Data) appear to lag behind by one day up until March 16, possibly because WHO Situation reports are published at specific timings which don’t match accurately with government reporting timings. Also, these datasets miss the same update as the John Hopkins numbers (from 314 to 399 cases), they were not updated on March 17, and they appear to have a typing error in them (1.085 cases on March 16, while the official government number was 1.058).

Finally, Our World in Data temporarily stopped updating beyond March 17 because WHO shifted their reporting window: up until Situation report 57 the observed 24-hour time window ended at 10 a.m. CET, since then it ends at midnight. This causes a small overlap making it difficult to accurately compare data and analyze trends.

• Update March 23: Note that Our World in Data stopped relying on WHO data as they found too many errors in the daily Situation reports. Instead, they switched to data provided by the ECDC.

In summary, John Hopkins University data most closely matches official government numbers (for Belgium).

Total number of confirmed COVID-19 cases in Belgium in March 2020, comparison between different sources.

If you are looking for alternative data sources, direct reports by governments, or data on specific regions or cities, I highly recommend the data section of the Coronavirus Tech Handbook, a crowdsourced document bringing together all the tools, datasets and visualizations on this topic.

The sheer amount of available data can make it a bit overwhelming, especially taking into account that new numbers are being announced almost constantly. When in doubt, I would advise to stick to the four most complete data sources listed above.