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)