Graphs day 83, pandemic day 89, day 160 since the first cases were diagnosed.
I’ll continue to update the COVID-19 case and death numbers as long as the global pandemic continues, but as life begins to settle into the new normal in the U.S., I am settling into the new normal of posting a longer, more substantive article every Monday, Wednesday, and Friday.
I would love nothing more than to go back to posting about Australian rules football, but reality seems to be conspiring to keep COVID-19 and racial justice in the news. So today, we examine another way of visualizing COVID-19 cases, one that gives a clearer picture of how the disease is moving around the world.
COVID-19 worldwide statistics
Before I launch into the new visualizations, let’s look at the statistics we have been charting since the beginning of the pandemic, and the predictions we have been tracking since May 17th.
Total cases of COVID-19 diagnosed worldwide: 6,958,337
Total deaths: 401,487
Today is the day we hit 400,000 deaths worldwide, four days after my prediction. The next major milestone is 7,000,000 cases worldwide. I had predicted we would reach that milestone on June 17th, but it looks near certain that we will actually reach seven million tomorrow, a full eight days early.
Deaths per million people by country
The graphs that I have been showing from the beginning have shown the total number of cases and deaths, either worldwide or in specific countries. As I have mentioned before, this is definitely the right way to look at deaths, because once someone dies of COVID-19, they’re not coming back to life. Each new death just adds to the COVID-19 death toll.
So I’ll break from my usual order and show deaths before I show cases. Deaths per million people for selected countries shown on a map:
and as a graph showing increasing death from mid-February to today:
A new look at case rates
In many ways, the steadily-increasing graphs I’ve been making are the right graphs for case rates too. It is absolutely a meaningful question to ask “how many people have been diagnosed with COVID-19 since the beginning of the epidemic, and how does that vary between countries?” Answers can provide extremely useful data for planning nationwide responses.
But in another way, these cumulative case graphs offer a misleading picture of the current state of the pandemic. The dead stay dead, but people who are sick with COVID-19 can get better. Although it’s still not clear whether having COVID-19 confers immunity to future infection, it is both meaningful and encouraging to plot how many people are currently sick. While it’s hard to know who is sick at any moment, it’s quite easy to plot the number (or rate) of new cases diagnosed.
To recap: for each day on my graphs, I have been plotting the total number of cases (overall or per million people) that have been reported as of that day. For today’s graphs, I will instead plot the number of NEW cases reported each day.
IMPORTANT NOTE: These graphs CANNOT be directly compared with the cumulative case graphs that I have been making since March 18th. Please don’t even try to compare them. They show the same information in a completely different way.
One possible downside of this approach is that the number of new cases reported on a particular day is subject to large day-to-day variations for a variety of reasons including statistical dumb luck. These daily variations are quite interesting and will be the subject of a future post. But they make it difficult to see overall trends, so I am “smoothing” the curves by replacing each day’s case data with the average value itself, the 5 days before, and the 5 days after. This results in a curve that has approximately the same value as the real curve everywhere, but with the daily variations removed so the curve looks much smoother.
It’s easier to explain with an example: the graph below shows the number of cases of COVID-19 reported worldwide, every day since outbreak began in Wuhan, China. The blue line shows the actual number of cases reported each day; the red line shows the smoothed curve.
In case you think I’m doing something weird to try to mislead you: no I’m not. This is a very common method in all areas of science. Also, look at the curves, they clearly look about the same.
For the rest of the graphs in this post, I will show ONLY the smoothed data. But it’s easy to create graphs using the observed data instead. If you’d like to try, download my Excel template linked below (version 4) and send me an email (firstname.lastname@example.org). I’d be glad to show you how.
So how do our usual “Big 10” countries look with this new visualization approach? Confusing. Don’t try to make sense of the 10 overlapping lines in the graph below, just enjoy how confusing it is.
Clearly we need a better approach – and fortunately, our better approach will also allow us to consider some of the countries we have looked at previously.
The only thing I’d like you to notice about the overlapping curves on the graph above is their general shape: the curves rise, reach a peak, and fall. The peak can be narrow or wide, high or low, and it can occur at different times. For some countries, the peak has yet to arrive. (Note: You might be tempted to call these “bell curves,” because they are vaguely bell-shaped – but the term “bell curve” refers to a specific shape of curve, which these are not.)
The shapes of these curves allow us to divide countries into four general categories based on how the epidemic has evolved there. Below, I show the curves for the countries in each category, and briefly explain what they mean. Over time, we’ll follow these categories as different countries move in and out of them. All graphs have the same scale except where noted.
Countries where the epidemic was quickly and easily contained
I’m deliberately showing these countries on the same scale as the countries in other categories (the vertical axis going from 0 to 275 cases per million people); it really drives home how well these countries kept cases to a minimum. All three are found in Asia: China, South Korea, and Japan. For this graph only, the horizontal axis starts in late January, otherwise China we would miss nearly all the cases in China.
Whatever these countries did to contain the epidemic, they did it right. COVID-19 could return anywhere, but it seems unlikely in these countries.
Countries where the epidemic is currently under control
In these countries, the curve has risen, peaked, and fallen to near zero – meaning that very few new cases are currently being reported in these countries. They are all in Europe. In the order in which the peak arrived, they are: Italy, Spain, Switzerland, Slovenia, France, and Belgium.
These countries struggled to contain the epidemic, but finally won – at least for the moment. That’s why I say “currently under control.” Antlers crossed that COVID-19 remains under control in all these countries.
Countries where the epidemic is mostly headed in the right direction
The next category contains countries where the case rate seems to have peaked, and the curve appears to be on a downslope – meaning fewer and fewer cases are being reported each day. These countries are: the United States, the United Kingdom, Russia, Belarus, Peru, and Qatar. This is the only graph with an inset, otherwise Qatar would have been literally off the charts. The vertical axis of the inset graph goes from zero to 700 cases per million.
These countries have taken concrete steps to fight the pandemic, and those steps are working. If they continue on their current course, they will join the list of countries that have the disease under control. The only thing that worries me is that the decrease has stopped in the U.S. It’s not going up, but it’s not going down either. Hopefully it will work its way back down soon.
Countries where the epidemic is getting worse
Last and unfortunately most are those countries where the epidemic continues to get worse: Chile, Saudi Arabia, Brazil, Sweden, and India:
I’ll continue making these kinds of graphs in future updates.
Want to try out some other ways for yourself? You can get the data that I used to make these graphs from the European Centers for Disease Control’s Coronavirus Source Data; choose “all four metrics.” The Excel template that I used to make the plot of deaths is version 3.3. The plots of new cases by day come from version 4. You want the sheet that says “BIG TEN,” starting at column BI. I’d love to see what you can build with it, and I’m happy to help you figure it out! You can reach me by email at email@example.com, or leave a comment here.
Update tomorrow, and every day after that until this pandemic comes to an end. And coming Wednesday: finally a break from COVID-19 and racial justice for some imaginary sports.