The Visual Evolution of the “Flattening the Curve” Information Graphic

Communication has been quite a challenge during the COVID-19 pandemic, and data visualization hasn't been the most helpful given the low quality of the data – see Amanda Makulec's plea to think harder about making another coronavirus chart. A great example of how to do things right is the widely-circulated Flatten the Curve information graphic/cartoon. Here's a look at the work it is built on and how that has evolved from a figure in an academic paper to one of the clearest pieces of visual communication in some time.

The Cartoon

The information graphic was created by epidemiologist Dr. Siouxsie Wiles and illustrator Toby Morris for the New Zealand publication The Spinoff. I think it's fair to call this a cartoon, and I mean that in the best possible way.

Why Does It Work?

I think what makes this infographic work is the combination of a few key elements: a clear and straightforward message, a foundation on science, a clear tagline that you might call actionable, and enough visual elements to be informative enough without getting overwhelming.

To be clear, this is not data or a visualization. It's an illustration based on a conceptual drawing, which in turn is based on simulations. It's also a cartoon, and I don't mean just the two characters under the chart. The chart itself is basically a cartoon, and that's a good thing. Real data would be much messier and spikier, so a smooth and simple drawing works much better to get a message across.

People have wondered if the flatter curve should have the same area under it, but I think that's also missing the point. This is not data, it's showing the idea of what would happen in two different scenarios while trying to not get into the weeds of small details of the simulation, its settings, assumptions, etc.

If you want to get technical, there are also annotations on the chart: the line showing the healthcare system's capacity is a key to making the point here. And the little cartoon of the hospital nicely illustrates the idea of an overloaded system (and does it in a tasteful and subtle way – this might actually be my favorite part of the whole thing).

The Visual History

It's quite interesting to trace the history of this chart through a number of different stages. One early version looked like this, from a paper by Kelso et al. in BMC Public Health, published in 2009. It shows the results of simulations for different vales of R₀, which is the rate at which diseases spread between people. That value can be reduced by closing schools, etc., which is shown on the left here compared to not taking action on the right.

The rest happened very quickly. Fong et al. combined the two curves into one chart, which made the difference more apparent. This paper is still only pre-published in the May 2020 issue of the Emerging Infectious Diseases publication by the CDC (though as David Napoli points out on Twitter, a similar chart was published by the CDC in 2007).

Dalton et al. then added another crucial element, the conceptual line showing healthcare system capacity. This has a publication date of March 5, 2020, on SSRN (which appears to be a sort arXiv for social sciences research).

From here, it's easy to see how this turned into the iconic graphic (there's also some interesting backstory in this Fast Company piece). However, I don't think it would haven been nearly as effective without the three simple words Flatten the Curve written across the top. I believe we'll be looking back at this as a prime example for effective visual communication for a while.

Simulations Instead of Data?

Some of the papers mentioned above are based on simulations. Harry Stevens at the Washington Post has created a fantastic article with several simple simulations that show different strategies for containing an outbreak and what their effects are. The simulations are easy to follow and I especially love the charts of healthy, infected, and recovered people that build up as the simulations run.

Given the current state of testing, especially in the United States, the current data is extremely unreliable. I'm afraid visualizing this low-quality data is not going to be of much use. Simulations and information graphics or cartoons, especially done in a smart and tasteful way like the Flatten the Curve cartoon, are a great alternative.