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Graphs day 109, pandemic day 116, day 186 since the first cases were diagnosed. Today, Earth has passed 11 million total cases of COVID-19.

Total cases of COVID-19 diagnosed worldwide: 11,074,878

Total deaths: 525,121

Happy Fourth of July! In honor of U.S. Independence Day, we’ll take a closer look at the pattern of cases and deaths in the United States.

Total cases of COVID-19 diagnosed in the United States: 2,794,153

Total deaths: 129,434

Here’s the map of the total number of cases reported from the beginning of the epidemic in the U.S. This is total numbers rather than per capita, so naturally the larger states show more cases. The slight difference in the total is due to the fact that the number above includes the U.S. territories of Puerto Rico, Guam, the Virgin Islands, and the Northern Mariana Islands (American Samoa has had no cases).

The total amounts shown on the map above show the total impact of the epidemic in the U.S. from the beginning. Another way to look at the cases reported yesterday, giving us an idea of the state of the epidemic right now. Notice which states are experiencing the most cases right now. How is this map different from the map of total cases above?

Three days ago, I showed, for the first time, graphs of cases over time in the U.S., in the style of the global graphs I’ve been making all along. I described several categories of states and showed two graphs: states where cases peaked early in the epidemic, and states where cases were low at the beginning, but where the epidemic has been getting worse quickly recently. I had shown several states in each category, which resulted in very complicated graphs.

I realized it would be clearer to show just one or two states in each category. And so – note that these numbers *are* per million people:

I showed a state that peaked in early April (New York), a state that peaked in May (Maryland), two states that have gotten much worse (Florida and Arizona), a state that started bad and is getting worse (Louisiana), and a state that has stayed fairly low throughout (Ohio).

One state I haven’t shown on this graph is the Granddaddy of Them All, California. The curve for California follows that of Ohio, but at a slightly higher level. I’ll keep an eye on California as we go, and show them on future graphs.

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.

Another COVID-19 update coming tonight, but moose cannot live on COVID-19 updates alone. Today is part of this blog’s usual Monday-Wednesday-Friday schedule, so I’m getting back to posting about all the other fascinating things in the world.

Today, it’s a return to our amazing planet Earth, and the amazing tool that can show it to us in glorious detail, Google Earth. (Note: Today’s post is possibly slightly NSFW, depending on your workplace’s policies and whether you work for Wang Laboratories.)

There’s an old saying about my home state of Florida. I’ll let Homer Simpson tell it:

That 2000 episode of The Simpsons popularized the saying, but I heard people refer to Florida as “America’s Wang” as early as the 1980s. Because, I mean, just LOOK at it, hanging there wangishly in the southeast corner of the U.S.:

and zooming in for a closer look reveals its full wangliness:

But zoom in again to unzip the truth about one of Florida 67 counties… Pinellas County is Florida’s wang!

Pinellas County, home of the cities of Clearwater and St. Petersburg, is a peninsula that hangs flaccidly down from the rest of Florida, bounded by Tampa Bay on the east and the Gulf of Mexico on the west. The whole southern tip of the county looks a bit wanglike, actually, but there’s one area in particular I’d like you to focus on.

Like many coastal regions all over the world, Pinellas County features a system of barrier islands, formed by the waves and tides along the beach. These islands tend to be long and thin, and when they are oriented just right… well, take a look at Long Key, the Wang of Pinellas County:

But we’re still not done here, because take a close look just to the right of the red pin marker on Long Key to see this:

The outline shows the neighborhood of Belle Vista Beach, a wealthy enclave of large single-family homes in St. Pete Beach, Florida (and until 1957, a city in its own right before being merged the rest of St. Pete Beach). Looks like a lovely and wangly place to call home.

But we’re still not done, because on the southwestern edge of Belle Vista Beach, enclosed by McPherson Bayou, is a small protuberance called Mangrove Point:

…and thus we have reached the tip.

If you were to visit the very tip of America’s Wang’s Wang’s Wang’s Wang’s Wang, what would you see? Amazingly, Google Earth can take us there:

And it can be yours!

I didn’t plan it this way, and I didn’t even know when I started writing this post, but the pink house in the middle is for sale. For just under $1.7 million, you can own this 4-bedroom, 4-bath, 3000-square-foot (270 square meter) house at 3 Mangrove Point in St. Pete Beach, Florida – at the tip of all the wangs!

When the house sells, I’ll remove this part of the post and end at the street view of Mangrove Point, so I don’t ruin anyone’s privacy.

But until then, a message to whatever unfortunate real estate agent is listing the property: I’m sorry and congratulations, this is the weirdest free advertising you will probably ever get.

Stay tuned tonight for a COVID-19 update, and stay tuned over the coming weeks for the return of favorite series like Except They Weren’t, and for some exciting new series. Stay safe and stay curious!

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.

Graphs day 105, pandemic day 112, day 182 since the first cases were diagnosed.

I had a full update today, on both global and U.S. state data – complete with a shiny new data source.

Then I accidentally deleted the Excel worksheet with all my formulas.

brb drinking

Update tomorrow, and every day after that until this pandemic comes to an end or I lose my mind, which is looking much more likely after today.