Graphs day 107, pandemic day 114, day 184 since the first cases were diagnosed. Quick update of the global data today. There are many advantages to using the JHU COVID-19 dashboard data, but the disadvantage is that it’s one day behind, so the data you will see goes up to July 1st.

Total cases of COVID-19 diagnosed worldwide: 10,694,060

Total deaths: 516,210

Worldwide cases and deaths

The number of cases reported worldwide every day just keeps going up up up:

I’m not showing the cumulative deaths plot today, because I’m sometimes afraid I’m overwhelming you with plots. But deaths keep going up too. My original prediction said we would hit 600,000 deaths worldwide three weeks from today, on July 23rd. Now it looks like it could be Monday, July 20th.

The global case fatality rate now is now at 4.8 percent, down from where it was a month ago. Unfortunately, this does not mean that we are getting better at treating COVID-19. We’re not. It simply means that COVID-19 is spreading among younger populations who are a bit more likely to survive, and live on with major health problems.

Cases and deaths by country

No changes in our usual four categories of countries today, but there are some countries we’re keeping an eye on, and if they continue on their current trajectories over the weekend, I might move the move them into another category.

Countries where COVID-19 was quickly contained

Usual graph style for all today’s graphs: each country is color-coded and labeled, labels include total deaths per million people from the beginning, label sizes and line thicknesses represent the case fatality rate.

There’s a hint of an uptick in cases over the past week in Japan and Australia, but they’re both still under three cases per million people. That certainly still counts as quickly contained.

Countries where COVID-19 is now under control

Maybe a hint of an uptick in France today, but we’ve got a long way until cases in any of these European countries get close to the levels we’re seeing in other countries.

Countries that are headed in the right direction(-ish)

Same graph style as before, with the addition of the “Qatar-scale inset” going from 0 to 700 cases per million people.

Cases in the United Kingdom are still going down, but they stubbornly refuse to fall below 10 cases per million people. On the other end is Saudi Arabia, which seems to be getting worse.

Countries where the epidemic is getting worse

Chile is still displayed on the Qatar scale, but they’re back down to 200.3 cases per million people. They’ll be back on the main scale soon.

Chile definitely counts as headed in the right direction. If they stay that way through the weekend, they’ll move to the Headed in the Right Direction category. It also looks like Mexico might be getting better. On the other end, the U.S. is looking worse than ever.

Want to try out some of these graphs for yourself? You can get the data that I used to make the country graphs from Johns Hopkins University’s Center for Systems Science and Engineering (CSSE) COVID-19 data site. Click on csse_covid_19_data, then on csse_covid_19_time_series, then download all the CSV files. Or clone the whole repository in GitHub.

You are welcome and encouraged to use my Excel templates. They’re now at version 5, and I have two separate templates: a global data template and a U.S. state data template.

Update tomorrow, and every day after that until this pandemic comes to an end or I lose my mind.

Graphs day 106, pandemic day 113, day 183 since the first cases were diagnosed. Longer post today with new analysis, looking at trends in cases and deaths by state here in the U.S.

Total cases of COVID-19 diagnosed worldwide: 10,475,826

Total deaths: 511,253

Since we’re about to look in more detail at the U.S., here are the same pair of numbers for the land of baseball, apple pie, and coronavirus.

Total cases of COVID-19 diagnosed in the United States: 2,635,417

Total deaths: 127,417

Before I get into the results, a quick methodology announcement: I have changed my primary data source, I am now using the dataset of the Johns Hopkins COVID-19 dashboard. This dataset has a number of advantages; I’ll talk about those advantages in a future post. Leave a comment if you’re curious.

Last week, I showed a map of the U.S. with each state’s COVID-19 rates. That map will return soon, but today I wanted to show a different kind of analysis: trends in cases in states over the past three months.

I loaded the state-by-state case numbers from JHU into an Excel spreadsheet similar to the one that I’ve been using for the global data. The outcome was a plot of cases per million people in each of the 50 states and five U.S. territories – which I’m not even going to try to show you because it’s too confusing.

But it soon became clear that the timelines lined up into a few patterns. By creating a separate graph for each pattern, and combining similar-looking curves for some bordering states into one curve, I was able to create some graphs that are manageable (although still a bit too dense).

I’ll show all the patterns in subsequent posts, but today I want to focus on two: states where cases peaked early and have since gotten their epidemics under control, and states where cases were fairly low early but are now skyrocketing to scary and dangerous levels.

No doubt you have heard this story in the news, and you can probably guess which states are in each category. But it makes a different impression when you can see the graphs with your own eyes – or make the graphs with your own hands using my templates, linked at the bottom of this post.

States where cases peaked early

Here is a graph of cases per capita in 10 states (Connecticut and Rhode Island are combined into one curve). It has the usual format – each state is a separate line, color-coded and labeled. The numbers on the labels are total deaths per million people since the starts of the epidemic. Line thicknesses, label font sizes, and label border thicknesses all scale with the case fatality rate in the state. The vertical scale is the same as usual: zero to 200 cases per million on the main graph, zero to 700 cases per million on the “Qatar-scale inset.”

Yes, this graph is too complicated, and yes, it will be improved in the coming days and weeks. If you have suggestions on what would make it more readable, and/or on other things that would be interesting to graph, let me know in the comments.

The thing to notice in this graph is the overall shape of the curves – all these states had peaks in cases in April, and have far fewer cases now. Cases are increasing in many of them, but they are nowhere near the level they were before.

Keep that shape in mind as you see the next category.

States where cases are getting worse, quickly

Same style of graphs, same scales, seven other states. And the graph looks very, very different:

What are states like New York and Massachusetts doing that states like Arizona and Florida are not doing?

Want to try out some of these graphs for yourself? You can get the data that I used to make the country graphs from Johns Hopkins University’s Center for Systems Science and Engineering (CSSE) COVID-19 data site. Click on csse_covid_19_data, then on csse_covid_19_time_series, then download all the CSV files. Or clone the whole repository in GitHub.

You are welcome and encouraged to use my Excel templates. They’re now at version 5, and I have two separate templates: a global data template and a U.S. state data template.

Update tomorrow, and every day after that until this pandemic comes to an end or I lose my mind.