Over the past year, our response to COVID has been influenced by a series of data visualizations that have evolved and mutated faster than the virus itself. March and April of 2020 saw a brief explosion of efforts to both understand and explain the emerging pandemic.
In mid-March of 2020, Information Is Beautiful published a COVID Infographic that went beyond infection and mortality rates. In an easy-to-read format, the Infographic began to answer our most pressing questions. What’s my relative risk if I go grocery shopping or attend a neighbor’s BBQ? How safe is it to get a haircut?
By early April 2020 we lived in a state of data model mania. When will the virus go away? Where is the pandemic rampant? Should I be concerned? Can our powers of computing and mathematics shed any light on this unprecedented terror? The Financial Times published an attempt to predict the future, predictably reflecting little more than our state of panic and the active dialog between mathematicians, scientists and the public at-large.
The media was quickly flooded by parabolic curves and lines of all shapes and sizes. Leading publications, such as The New York Times, began to clean up the statistical and visual clutter. The real problem became evident immediately. We really had no idea what to expect in the near future. In this chart, a handful of predictive models from well known institutions are clearly labeled, and standard measures like a 7 day average begin to emerge. But as this example illustrates, beyond a sense that mortality would decline in a few months none of our leading institutions could really agree on what was going to happen next (or when).
In the Spring of 2020 the reality of COVID began to settle in and communities around the world began to enforce restrictions or outright lockdowns. Visualizations of COVID in the media shifted from predictive to descriptive. And in the examples below from The New York Times, tabular data of accepted measures (Daily average in the last 7 days) begin to use standards for comparison and sparklines. Cases are expressed per hundred (or thousand) members of a population. Color coding is used to create urgency, and express a relative safety by community. A similar technique can be seen in Warming Stripes, a very effective visualization of climate change.
Today, the COVID pandemic is an extension of our daily weather report. You can look up the severity in your local community over morning coffee. Forecasting has taken a back seat. Focus is now on the rise of vaccination. Information is Beautiful updates its Infographic with data such as vaccine rates by country and mask effectiveness. And the media have largely standardized visualizations of COVID in a line chart or histogram like the example below from the New York Times.
With multiple factors influencing vaccination rates, such as supply, vaccine hesitation, and distribution ability, we face a new round of complexity and uncertainty. When will I receive a vaccine? Why is there such an uneven distribution of vaccine? And in response Journalists are experimenting with new visualizations. In this example, no less than seven different data points illustrate the relative difficulty (or level of concern) in obtaining a COVID vaccine.
A year ago, on my way home from my last visit to the gym, I stopped by our local CVS and noticed older customers wearing latex gloves while handling vitamin bottles. I shivered momentarily at the checkout touchpad and asked the clerk to take my card and approve my purchase. Some virus was in our midst…somewhere.
Today I drive to my supermarket, don a mask, and do a six-foot dance around the other customers hunting for arugula and bananas. I splash disinfectant on my hands like aftershave once the groceries are loaded in the back of my car. I can almost count the weeks until my vaccination.
COVID is still in our backyard. But we know where it lurks and how to stay healthy due in part to the effective visualization of reliable data, governed by the time-tested principles of sound Journalism.
