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.
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