Graphs day 118, pandemic day 125, day 195 since the first cases were diagnosed. Today Florida is in the spotlight!
Total cases of COVID-19 diagnosed worldwide: 12,910,357
Total deaths: 569,128
Yesterday I asked a simple question: if Florida were a country, how screwed would it be? Or: how do cases in Florida – just Florida, separate from the rest of the U.S. – compare to cases in the countries we’ve been following?
This has been an adventure, I basically had to start over with my spreadsheet to make this work. Fortunately, I am very very stubborn.
Usual graph style, Florida is in green. Blue is no longer the entire U.S., it’s now the U.S. minus Florida. (I should really make a page that explains the graphs for people starting the blog from this post.)
Countries where COVID-19 was quickly contained
History of the COVID-19 pandemic in various countries, and also Florida (click for a larger version)
To answer the question raised above: quite screwed. Look how fast the pandemic in Florida Looney Tuned off the main scale onto the Qatar graph. Florida is now leading all the countries on this graph in terms of cases per million people.
The good news is that the case fatality rate in Florida is lower, at just 1.6 percent. But that’s mostly illusory good news – so many new cases have been transmitted in the past two weeks that many people who will die haven’t reached the point of dying yet. Expect the CFR to climb to close to its value in the rest of the US.
Reminder: I’ll be camping in Shenandoah National Park the rest of this week, so no more COVID-19 updates until Saturday. But you’ll still see non-COVID posts on Wednesday and Friday this week.
Graphs day 117, pandemic day 124, day 194 since the first cases were diagnosed. I haven’t forgotten about “what if Florida were a country?,” it’s just proven more difficult than it first appeared. But I think I got it now, and I’ll tell you about it tomorrow.
Total cases of COVID-19 diagnosed worldwide: 12,717,921
Total deaths: 565,139
Cases by country
Today is Independence Day in the African island nation of Sao Tome and Principe. Conversely, Mongolia has no Independence Day – they’re the country that you celebrate your independence from – but this week is Naadam, the biggest holiday in the country.
MONGOLTAGE! (thanks John Green)
So today we’ll include both those countries in the graphs. They have just 727 and 230 cases respectively. It’s good to be an island nation, and it’s good to have the lowest population density in the world.
Usual graph style, usual four categories of countries. (I should really make a page that explains the graphs for people starting the blog from this post.)
Countries where COVID-19 was quickly contained
Countries that quickly contained their COVID-19 epidemics (click for a larger version)
You read that right, there have been zero deaths from COVID-19 in Mongolia (gold line). Happy Naadam!
Countries where COVID-19 is now under control
Countries where COVID-19 is currently under control (click for a larger version)
Sao Tome and Principe (pink) looks worse than it is because there are only 200,000 people in the entire country, about the size of Tallahassee, Florida or Arlington, Virginia. But after a few spikes, COVID-19 appears to be fully under control for now, just as it is in the European countries shown on the same graph.
Usual caveat: under control, for now. Let’s hope it stays that way.
Countries that are headed in the right direction(-ish)
Great news – welcome back to the main graph, Qatar!
Countries where newly-reported cases per million people are steady or decreasing (click for a larger version)
There was an uptick of cases in Sweden yesterday. Are they going to fool me again? It’s only been two days of increase (from 43 cases per million people on Thursday to 57 cases per million people yesterday), so I’m still hopeful that cases there will start decreasing again soon.
Countries where the epidemic is getting worse
Countries where the epidemic is still getting worse (click for a larger version)
The good news is that cases in Serbia, India, and Australia seem to have stabilized for the moment – still new cases being diagnosed each day, but at a steady rate. The bad news is that South Africa has now taken over top spot on the chart, and they did it far more swiftly than previous leaders USA and Brazil.
This week in this blog
Heads up, I’m going camping from Tuesday to Friday, so tomorrow will be the last regular COVID-19 update until Saturday. But I’ll still have regular posts for you on Monday, Wednesday, and Friday.
Tomorrow: what if Florida were a country? How f*cked would that country be?
Coming up later this week: a special guest post from everyone’s favorite special guest blogger, the Grass Mud Horse!
How to do it yourself
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 from GitHub.
You are welcome and encouraged to use my Excel templates. I’ve tinkered enough that I’m plus-plusing the version to 5.1. I have two separate templates: a global data template and a U.S. state data template.
Update tomorrow, then not again until Saturday, then again every damn day until this damn pandemic ends. Sigh.
Graphs day 115, pandemic day 122, day 192 since the first cases were diagnosed. Happy Independence Day to the Bahamas, but there are very few cases in the Bahamas.
Total cases of COVID-19 diagnosed worldwide (another sad milestone): 12,268,518
Total deaths: 554,924
Quick global update to tell you that, sadly, the case rate in Australia has reached half of its former peak value, so I’m moving Australia into the getting worse category. They are the grey line below.
Australia (gray line) is getting worse. Click for a larger view.
Coming up tomorrow: what if Florida were a country?
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 from GitHub.
You are welcome and encouraged to use my Excel templates. I’ve tinkered enough that I’m plus-plusing the version to 5.1. 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 114, pandemic day 121, day 191 since the first cases were diagnosed. Happy Independence Day to Argentina and South Sudan, I’ll include you in today’s global update.
Total cases of COVID-19 diagnosed worldwide (another sad milestone): 12,041,480
Total deaths: 549,468
Cases by country
It’s been a long time since I’ve shown a map of the COVID-19 death toll, so I’ll show that at the end of today’s post. But first, our usual categories.
As always, the graphs show dates on the horizontal axis, cases per million people on the vertical axis. The lines show new cases reported *on that day*. Each line is color-coded and labeled with the name of the country it represents; labels also include that country’s deaths per million people (dpm). Line thicknesses and label sizes represent the case fatality rate in that country (deaths divided by total cases).
Countries where COVID-19 was quickly contained
Countries that quickly contained their COVID-19 epidemics (click for a larger version)
South Sudan (pink) is one of the world’s poorest countries, but they have managed to keep COVID-19 cases fairly low, primarily due to the lack of mobility of the population and lack of international travel to and from South Sudan. Meanwhile, cases continue to increase in Australia: yesterday 170 new cases were reported, for a smoothed per million people rate of 6.5 cases per million Australians. I said that if the case rate exceeds 6.8 per million, I’ll move Australia into the “getting worse” category. That could happen tomorrow, we’ll see.
Countries where COVID-19 is now under control
Countries where COVID-19 is currently under control (click for a larger version)
No major changes to the “under control” category this week.
Countries that are headed in the right direction(-ish)
I knew I said I’d wait another day before moving Sweden into the “countries headed in the right direction” category, but the level of cases reported is back down to the level of late May.
Countries where newly-reported cases per million people are steady or decreasing (click for a larger version)
There’s no way to know whether Sweden will stay on its current course, or whether another wave will begin and send cases higher than ever before. But for now at least, Sweden looks pretty good.
Countries where the epidemic is getting worse
It’s Nueve de Julio in Argentina, so we’ll check in with them on today’s graph (ilght blue.
Countries where the epidemic is still getting worse (click for a larger version)
Unlike in their neighbors to the west, Chile, the epidemic in Argentina continues to get worse. Case growth has slowed down in Serbia and Brazil, but continues with distressing speed in the U.S. and South Africa.
Deaths per million people by country
And because I like to check back in every once in a while with things we’ve seen before, here is a map of deaths per million people in countries around the world. These are total deaths since the beginning of the epidemic, because the dead stay dead.
Deaths due to COVID-19 per million people, colored according to the color scale at the bottom. Click for a larger version.
and a close-up of Europe and the Americas:
Deaths due to COVID-19 per million people, close-up of Europe and the Americas, with countries and death rates labeled.
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 from GitHub.
You are welcome and encouraged to use my Excel templates. I’ve tinkered enough that I’m plus-plusing the version to 5.1. 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.
The first casualty of COVID-19 in the American sports world was the cancellation of the 2020 NCAA men’s basketball tournament. “March Madness” is one of the joys of the American sports calendar, and this year, it was gone.
Aw crap, not these guys again (the 2020 Duke men’s basketball team)
I decided to take up the challenge and simulated the entire 2020 tournament – from announcing the teams to playing the final. Sadly, Duke won. And then I had an even better idea.
The NCAA Cup is the college basketball tournament for everyone, designed after European soccer tournaments like England’s FA Cup. Every team qualifies. At the beginning of each round, all matchups for that round are selected randomly.
I simulate each game on the whatifsports.com college basketball simulator, and announce the results in real time on Twitter at @fixthemadness.
Winners advance to the next round, and the process repeats until one team wins it all. The NCAA Cup has been going on since early April. I’ve given an update once before, but now it’s time for a more thorough update.
Rounds 1 and 2 are in the books, and round 3 has just started. I won’t cover everything here, but you can see the full and complete results on my NCAA Cup page.
Round 1
This is probably what it looked like when Navy won the play-in game
Round 1 began with the play-in game on April 7th, the day after Duke won the imaginary 2020 NCAA tournament. Because there were an odd number of teams (351), two teams were randomly selected for a play-in game Navy upset Virginia Commonwealth to advance to the first round proper, along with every other team.
Each of the 350 remaining teams was randomly paired one against another, and the home team for each was selected randomly too. That made 175 games, which were played between April 9th and May 31st.
On May 9th in Las Vegas, UNLV shocked overall number one seed Gonzaga 82-71, behind 27 points by sophomore guard Bryce Hamilton. The Runnin Rebels’ tight defense held Gonzaga’s star power forward Filip Petrusev to just 15 points. Gonzaga, which had come into the Cup as the favorite to win it all, was knocked out at the first stage. It was an upset on par with UMBC’s win over Virginia in 2018.
I wish I had video of the UNLV’s win over Gonzaga, but it was imaginary. It probably looked like this, though.
The last first round game was held on May 31st at 10 PM ET, when Lamar beat Washington State 90-81.
The 175 first round winners advanced to the second round.
Round 2
Once again there were an odd number of teams (175), so a play-in game was required. In the play-in game, Radford beat Toledo. Radford, along with the rest of the round 1 winners, advanced to the second round. Once again, all the matchups and home teams were chosen randomly.
This is probably what it Luka Garza looked like after his team lost to Central Arkansas
The next day, in a matchup of top 10 teams, Louisville beat Florida State 72-68. The rest of the second round went mostly as expected, until…
Remember UNLV? In the first round, they pulled off a stunning upset, knocking off top seed Gonzaga. In the second round, the same thing happened to them, as the Runnin’ Rebels lost on the road to the #302 team in the NET rankings, the Longwood Lancers. Jaylon Wilson scored 16 points for the Lancers, who won 65-55.
The 87 second round winners advanced to the third round.
Round 3
There were 87 winners of round 2 – one again an odd number, so once again a play-in game. East Tennessee state beat Oakland in the play-in game to advance to round 3 proper.
And that’s where we are now! One great game has been played already: #8 Louisville followed up its second round win over Florida State with a third round upset of #2 Kansas.
For the full list of round 3 matchups, see the NCAA Cup page. I won’t list them all here, but here are some of the most interesting games coming up:
Date and Time
Matchup
Saturday July 11th at 8 PM
(#302) Longwood at (#44) Virginia
Friday July 17 at 8 PM
(#22) Texas Tech at (#7) Michigan State
Saturday July 18th at 8 PM
(#49) Saint Louis at (#36) East Tennessee State
Sunday July 19th at 8 PM
(#16) Ohio State at (#4) San Diego State
Tuesday July 21st at 8 PM
(#39) Illinois at (#52) Notre Dame
Thursday July 23rd at 8 PM
(#295) McNeese State at (#6) Duke
Schedule of some of the most anticipated games of round 3
Who will win it all? The championship game is on Saturday, August 29th. Join us every step of the way at @fixthemadness!
Quick COVID-19 update
Lastly, a quick update on COVID-19 cases around the world and in New York State.
Graphs day 113, pandemic day 120, day 190 since the first cases were diagnosed.
Total cases of COVID-19 diagnosed worldwide: 11,829,602
Total deaths: 544,163
Here is the graph of countries where the epidemic is getting worse:
Countries where the COVID-19 epidemic is getting worse: number of cases reported per day vs. time. Labels show deaths per million people.
Things do seem to be getting better is Sweden. But they’ve fooled me before; let’s hope it’s for real this time? If the downward trend continues for another two days, I’ll move Sweden to the “headed in the right direction” category.
You know who’s not getting better? The United States and South Africa.
Lastly, I discovered I made a mistake in yesterday’s graph of cases in different parts of New York – my case fatality rates were wrong. The corrected graph, including data for today, is shown here. I’ll update yesterday’s post with the correct graph as well.
Cases per million people per day in three regions of New York state: New York City, Westchester County, and the rest of the state
NCAA Cup updates throughout the third round and beyond. COVID-19 updates until the end of the pandemic or until I lose my mind.
