Science communicators have been responding to the Covid-19 epidemic with enthusiasm, demonstrating innovative ways to explain complex issues to a wide variety of audiences. Over the next few weeks we’ll be featuring some examples that highlight some of the best and most imaginative practice.
Condensing a large amount of complicated data into a clear visualisation has always been a crucial component of communicating research. This technique has never been more important or more challenging than it is in the current worldwide Covid-19 pandemic.
From the iconic ‘flatten the curve’ image to real-time tracking of the virus spread and other highly creative ways of presenting data, Covid-19 has been a case study in effective data visualisation.
Flatten the curve
No graphic has become more recognisable in recent weeks than the ‘flatten the curve’ twin-peaked model, forecasting the number of Covid-19 cases over time. The image has an interesting history, with its evolution a classic example of how data visualisation is constantly tweaked and refined to better illustrate the point.
The image first originated in a research paper published by the Center for Disease Control (CDC) in 2007 on planning for a pandemic, succinctly titled Interim pre-pandemic planning guidance: community strategy for pandemic influenza mitigation in the United States: early, targeted, layered use of nonpharmaceutical interventions. This initial version of the image was titled ‘Goals of Community Migration’ and only showed the two iconic curves: a steep curve in the case of no intervention, and a gentler curve representing ‘community mitigation’, i.e. social distancing efforts. It did not include the phrase ‘flatten the curve’ or the healthcare capacity dotted line.
The image may have remained buried within the 2007 CDC paper forever if it had not been noticed by visual data journalist Rosamund Pearce at The Economist, who republished the image as part of her Covid-19 coverage at the start of the outbreak. This version was near-identical to the original CDC image and was captioned ‘Press down firmly: Intended impact of social distancing measures.’
The Economist article then caught the eye of Drew Harris, assistant professor at Thomas Jefferson University. In the past Harris had worked as a pandemic preparedness trainer, and used the CDC graph in his presentations when talking to hospitals. He had made one minor edit, however: he was the first to add the ‘healthcare system capacity’ dotted line, to illustrate the true benefit of the second curve. Reading The Economist article inspired Harris to share his slightly tweaked version of the graph on Twitter, and it instantly went viral for its clear simplicity.
The final step in the evolution of the image was fulfilled by Dr Siouxsie Wiles, a microbiologist in New Zealand. Dr Wiles wanted to connect the two different possible overall outcomes to people’s individual behavior, so she asked illustrator Toby Morris to add in characters representing two opposing attitudes: ‘Whatever, it’s just a flu’, vs ‘Don’t panic, but be careful’.
Morris reproduced the two-peaked graph, including Harris’s healthcare capacity line, and even added an element of animation to the curve. Now, viewers could watch the image highlight each of the two characters and see the graph transform to reflect the result: a steep curve for the dismissive character, a gentle one for the cautious character. This animated and illustrated version also included a new title: ‘Flatten the Curve‘.
This was the final step that propelled the concept to internet fame, and made both the image and the phrase the respective flag and motto of worldwide efforts to combat Covid-19. The graph of the viral then really went viral, featuring the obligatory internet cat.
Tracking the spread
Outside of ‘flatten the curve’, the second most notable example of data visualisation of the pandemic has been the multitude of attempts to encapsulate the geographic spread of the disease in real time. Various websites and countries have attempted to track the virus, with a few noteworthy stand-outs.
The data visualisation team at the New York Times, run by guru Amanda Cox, created an interactive map that allows you to ‘zoom in’ to a specific country or even county to see the number of cases and/or deaths, and its immense amount of data is free to download. Johns Hopkins University is the other major Covid-19 data curator, gathering information from various official and credible sources.
The free availability of much of this data has made it possible for many organisations to visualise the same data differently, aiming it towards different audiences. For example, there is also a more complicated coronavirus tracker geared specifically towards epidemiologists and other disease researchers.
Trajectory charts have become another widely shared data visualisation tool. These show each country’s daily death count, with their first cases of Covid-19 aligned, to compare how each country is doing at ‘flattening the curve’ and which countries are having comparatively more success at controlling the virus.
These types of charts and more can be found at Information is Beautiful, a data visualisation organisation featuring an enormous variety of tables and graphs that present many different aspects of the current pandemic – from the total number of deaths, to the virus’ current mortality rate, to the demographics of those who get ill and more. Drawing from the Johns Hopkins University data, Information is Beautiful has also done an exceptional job in streamlining the appearance of all these visuals, so that they are clear and crisp and many are interactive. (They also run annual Data Visualisation Awards if you want to browse hundreds of great examples on a wide range of topics.)
Even so, the argument has been made that daily death toll trackers are less helpful than you might think, at least to the average citizen. Much data visualisation still does not show if the number of cases or deaths is accelerating or decelerating, and if so, by how much. A single sum alone provides no indication if a country’s outbreak is coming under control or is raging unrestricted. Perhaps most importantly, much of the available data is actually not ‘actionable metrics’ – a chart showing a country’s steep trajectory doesn’t include a specific call to action for changed behaviour, but can cause anxiety and fear. It is therefore crucial to remember to keep data visualisations in context and for the surrounding explanations to emphasise the takeaway message.
Other creative ways of visualising the data
There is more to the story of this pandemic, however, than an exponential trajectory or a rapidly growing red circle over a city. Data visualisation has also been used to creatively illustrate other components of this crisis.
In an early attempt to reach members of the public who may still be skeptical about the need for social distancing, Harry Stevens’s Corona Simulator for the Washington Post created a clever way to run simulations of the disease right in front of the reader’s eyes. The animated model unfolds live, with moving colored dots representing people catching and spreading a virus. In an identical simulation with most dots refusing to move – to represent people social distancing – the virus’ spread is much slower. The random elements of the model means it calculates a slightly different example each time, but always confirms that social distancing flattens the curve.
Visual Capitalist tackled the same task, illustrating mathematically the importance of social distancing and how it drastically reduces the virus’ harms. Both of these visualisations are arguably more useful to the general public than daily death toll trackers, as they can clearly illustrate that social distancing does truly work and therefore can more directly influence behavior.
Other creative data visualisations include IHME’s closer look at healthcare capacity, comparing the growth of the virus to the number of hospital beds and ventilators each state has and is projected to need. Covid Act Now focuses on governmental response, taking the standard ‘flatten the curve’ image and adding in additional curves representing the possible measures that governments can put in place and how well they are followed or enforced.
Finally, data visualisations have also emerged on a slightly more practical and humorous note – such as TPfinder.co, an interactive map to help you find local stores that may still have toilet paper in stock. Thus data visualisation has been used both for sweeping worldwide snapshots and for very localised and hyper-focused concerns resulting from this unprecedented worldwide shift.
Data visualisation has proven to be an invaluable tool in the time of Covid-19, both to help study the disease itself and to actively encourage changes in behavior. Animated and interactive forms of data visualisation are becoming increasingly common and probably represent the future of data visualisation; yet static graphs can be memorable and effective as well, as shown by the ubiquity of ‘flatten the curve’.
Visualising the data resulting from one study so that the most important information is conveyed is difficult enough; visualising the data from a worldwide, rapidly changing crisis that interacts with many other factors is even harder. Yet even so, many people and organisations have done astounding work to visualise various facets of this crisis, and the lessons learned in data visualisation techniques and effectiveness will likely continue to inform research communication for many years to come.