Have you heard about the herd? (Daily COVID-19 data update CLIV)

Five moose, because Sweden
Sweden demonstrates its controversial herd immunity strategy

Graphs day 154, pandemic day 161, day 231 since the first cases were diagnosed. Today, let’s go back to the data from the JHU Coronavirus Resource Center and look at national-level graphs (since my version 7 spreadsheet has a template already set up for that).

Total cases of COVID-19 diagnosed worldwide: 21,879,368

Total deaths: 773,781

Happy Independence Day to two countries today: Indonesia (the world’s fourth-most-populous country) and Gabon (a country of two million people in central Africa with one of the world’s coolest national anthems). We’ll include lines for both countries in today’s post.

Usual graphs and labels for all five categories today. All five graphs are in the usual styles. The main graph is on the regular scale, from zero to 200 new cases diagnosed per day per million people – with the exception of the “getting worse” graph, which runs from zero to 300. Where there are smaller inset graphs, they are on the “Qatar scale,” which runs from zero to 700 cases per million people. Each country gets a unique color in each graph (although the colors can repeat across graphs). Line labels show the name of the region, and also the mortality rate (cumulative deaths per million people) in the region – cumulative because the dead stay dead. The thickness of the lines and the size of the labels depend on the cumulative case fatality rate – the number of people who died divided by the number diagnosed.

Regions where COVID-19 was quickly contained

Regions where COVID-19 was quickly contained

After 101 days with no local transmission of COVID-19, New Zealand experienced its first local case on day 102. There’s a very slight uptick in cases there and in South Korea, but keep it in perspective: the total number of cases in both places is still very low.

Regions where COVID-19 is currently under control(-ish)

Regions where COVID-19 is currently under control

France is still ticking depressingly upward. If cases in France reach 52 per million people per day – half of their peak in mid-April – I will sadly move them into the Second Wave category.

Regions moving in the right direction(-ish)

Regions where cases are decreasing(-ish)

Gabon is on this graph (purple line). Reported cases have gone up and down somewhat, but never above 75 cases per million people per day. Qatar had a bump within the past two weeks, but it appears to have passed for the moment. And unfortunately Sweden seems to be on an uptick again, but it’s still too early to tell if it’s a real increase or just random variation. Antlers crossed that Sweden’s herd immunity strategy is working, but it’s really not looking good at the moment.

Regions experiencing a second wave of COVID-19 cases

Regions experiencing a second wave of COVID-19

The second wave is maaaybe over in Australia, but keeps getting worse in Spain.

Regions where the first wave of COVID-19 continues to get worse

Regions where things are getting worse

I showed some different regions today. I’m showing Georgia on the main graph, but I forgot to swap them in for Florida on the Qatar scale inset, so you get a bit of both today. This is where Indonesia goes also. Cases are clearly increasing in Indonesia, but they are increasing very slowly and are still at a quite low level.

Coming up tomorrow: a break from the COVID-19 graphs and a return of the series about people and things who are Not What They Seem: enjoy a new episode of Except They Weren’t.

Want to give these graphs a try? Please do! Here is version 7.3 of my spreadsheet, which is just like version 7.2 but is now updated with data up to yesterday.

Pandemic updates tomorrow, and every day until the pandemic ends or I do.

Floridupdate (Daily COVID-19 data update CLIII)

Graphs day 153, pandemic day 160, day 230 since the first cases were diagnosed.

Total cases of COVID-19 diagnosed worldwide: 21,389,903

Total deaths: 772,373

Today’s Excel graph update comes from my weird home state of Florida. We’ve been following cases there for quite a while, but with the county health department data collected by Corona Data Scraper, we can look at patterns around the state in much greater detail. We’ll look at five urban areas: Miami, Tampa, Orlando, Pensacola – and Clewiston, a town of 7,000 people on the south shore of Lake Okeechobee. (Technically the data we’re looking at for the first four is for their metropolitan statistical areas, and Clewiston’s is for its micropolitan statistical area.)

Graphs are in the usual format, on the “Miami scale,” which runs from zero to 1,000 cases reported per million people per day. Urban areas are color-coded and labeled. Line thicknesses and label sizes are proportional to case fatality rates.

COVID-19 in some urban areas in Florida (click for a larger version)

I also looked at the Jacksonville metropolitan area, and it follows almost exactly the same trend as Orlando.

And speaking of Florida, the Wang Mansion has sold! It’s now listed as Pending on Zillow. Which one of you lucky readers purchased it? (If you don’t know what I’m talking about, see my post from July 2020, Wangception.)

Want to give it a try? Please do! Here is my new spreadsheet (version 8), although God help you until I document it better. The good news is that you should only need to change the worksheet called Graphs, and only refer to the sheet called Daily to get the codes for each country. Also be warned, it’s so big that it calculates sloooooooooooooooowly. You will probably want to go to Settings -> Calculation and change Calculation Options to Manual. Then the spreadsheet will only calculate its updated numbers when you tell it to, by pressing F5 on Windows or Shift-Enter on Mac.

Pandemic updates tomorrow, and every day until the pandemic ends or I do.

Pushing the Limits (Daily COVID-19 data update CLII)

Graphs day 152, pandemic day 159, day 229 since the first cases were diagnosed.

Total cases of COVID-19 diagnosed worldwide: 21,239,780

Total deaths: 767,692

I am continuing to use data retrieved by the Corona Data Scraper, which collects online reports from county, state, and national health departments all over the world. I am getting worried that I might be approaching the limit of what I can do with Excel running on my laptop. I could easily make these graphs in Python with SciServer or Kaggle, but that would be a wasted opportunity. There’s a reason I’ve been making these graphs in Excel all along – I want to show you how you can study the COVID-19 pandemic using a tool you probably already have and probably already know how to use.

If you know anything about how to speed up computation in Excel, or if you know of any other resources I can bring to this, please let me know! Comment here or reach out to me in one of the ways you know how to reach me.

Today’s graph compares some of the major global hotspots, and adding a new one today: the Atlanta metropolitan area.

COVID-19 in some of the hotspot areas worldwide

Just when it looked like the epidemic in Florida was getting better, cases are back up again in Miami. We’ll keep watching closely.

Want to give it a try? Please do! Here is my new spreadsheet (version 8), although God help you until I document it better. The good news is that you should only need to change the worksheet called Graphs, and only refer to the sheet called Daily to get the codes for each country. Also be warned, it’s so big that it calculates sloooooooooooooooowly. You will probably want to go to Settings -> Calculation and change Calculation Options to Manual. Then the spreadsheet will only calculate its updated numbers when you tell it to, by pressing F5 on Windows or Shift-Enter on Mac.

Pandemic updates tomorrow, and every day until the pandemic ends or I do.

Meanwhile in Australia (Daily COVID-19 data update CXLVIII)

Graphs day 148, pandemic day 155, day 225 since the first cases were diagnosed. And we’ve hit another milestone.

Total cases of COVID-19 diagnosed worldwide: 20,097,141

Total deaths: 748,616

A man lays down back-to-back with a kangaroo

Today, let’s take a look in detail at another country: Australia.

The epidemic in Australia has come in two waves. The first wave began in late February and peaked in late March. The second wave began in June and seems to be slowing a bit, but not even close to enough that we can declare it passed. But looking in more detail reveals a difference between the two waves; see the graph below. The first wave had cases all over Australia, while the second wave has been almost completely confined to one state: Victoria.

Australia consists of six states and three territories – yes, they’re called states rather than provinces. And like any county of states, there is plenty of good-natured trash talking among the states, and lately the other five states have had enormous fun at Victoria’s expense.

And while Victoria is the hot zone of Australia, let’s keep it in perspective. Victoria has about the same population as the U.S. state of Maryland. So as a comparison, I added Maryland to the graph, along with the usual comparison of Hubei Province in China.

The graph is below. We’re back to the usual format with labels, format explained below.

COVID-19 in Victoria (42 deaths per milion people), the rest of Australia (3 dpm), Hubei (78 dpm), and Maryland 596 dpm)

The main graph is on the traditional regular scale, from zero to 200 new cases diagnosed per day per million people. Each region gets a unique color in each graph: Hubei is blue, Victoria is green, the rest of Australia is red, and Maryland is purple. Line labels show the name of the region, and also the mortality rate (cumulative deaths per million people) in the region – cumulative because the dead stay dead. The thickness of the lines and the size of the labels depend on the cumulative case fatality rate – the number of people who died divided by the number diagnosed.

Victoria has had it far worse than the rest of Australia, but similarly-sized Maryland has had it even worse than that.

Want to give it a try? Please do! Here is my new spreadsheet (version 8), although God help you until I document it better. The good news is that you should only need to change the worksheet called Graphs, and only refer to the sheet called Daily to get the codes for each country. Also be warned, it’s so big that it calculates sloooooooooooooooowly. You will probably want to go to Settings -> Calculation and change Calculation Options to Manual. Then the spreadsheet will only calculate its updated numbers when you tell it to, by pressing F5 on Windows or Shift-Enter on Mac.

Pandemic updates tomorrow, and every day until the pandemic ends or I do.

Shop Local (Daily COVID-19 data update CXLVII)

Graphs day 147, pandemic day 154, day 224 since the first cases were diagnosed. Remember that Very Exciting New Idea that I was talking about last week? Here it is!

I’m going to skip the overall global numbers for today, because I want to make sure I understand what I’m presenting to you. I’m going the other way today – looking at numbers at an unprecedentedly local scale.

To enable this more focused look, I’ve changed data sources. I’m now using data provided by the Corona Data Scraper global citizen science collaboration. It’s not their data, of course, it’s our data – data reported by hundreds of national, state, and local health departments. The Corona Data Scraper simply crawls the Internet and downloads the latest data from each of these hundreds of local sources.

If you don’t believe the U.S. Centers for Disease Control (CDC) or the World Health Organization (WHO) – first of all, why? That’s like wanting to see a basketball game but refusing to watch anything from the NBA, the NCAA, the Olympics, high schools, Eurobasket, or the Chinese Basketball Association. But if you’re determined to not believe the experts, you no longer have to! All the numbers come from local sources all over the world.

This kind of distributed data collection is simple, but it’s not easy. Volunteers all over the world have spent countless hours writing software and communicating with health departments and other volunteers. And it hasn’t been easy on my end either. I’ve spent probably 35 hours the past two weeks – including almost all day on both Saturday and Sunday – turning Corona Data Scraper reports into an Excel spreadsheet. A four-sheet, 900,000+ line, 64 MB Excel spreadsheet.

And it’s all worth it to show you this graph.

It’s a much simpler version of the graphs that I’ve been showing you – the lines are about equal thickness and there are no data labels; instead I use different styles of dashed lines along with color to show you which regions are which.

The difference? Instead of showing the case rate for the entire countries of the U.S., China, Italy, and Switzerland, I show you the case rates only for specific, hard-hit areas of those countries. In the U.S.: the metropolitan areas of New York City (purple dotted line) and Miami (orange solid line). In China: the province of Hubei (blue long dash), whose capital is the infamous Wuhan. In Italy: the region of Lombardy, home of the Milan metropolitan area, the largest in Italy. In Switzerland, the canton of Geneva.

Cases reported per day per million people in each of five particularly hard-hit regions

I’m damn proud of this.

And this shows just one basic answer to one of the incredible variety and depth of questions that the Corona Data Scraper dataset can help us understand together.

Much, much, much more to come.

Want to give it a try yourself? You can get all the data from the Timeseries CSV link of the Corona Data Scraper website. Let me clean up my spreadsheet and share it as version 8 tomorrow.

Pandemic updates tomorrow, and roughly every day after that until the pandemic ends or I do.

19 million cases (Daily COVID-19 data update CXLV)

Graphs day 145, pandemic day 152, day 222 since the first cases were diagnosed. Remember that Very Exciting New Idea that I was working on last week? I’ve spent almost the entire weekend working on it, and It’s almost done – you’ll definitely see it this week! For today, here’s a quick update on COVID-19 numbers around the world.

Total cases of COVID-19 diagnosed worldwide: 19,637,506

Total deaths: 726,781

Happy Independence Day to Singapore, we’ll include their numbers in today’s graphs. And some countries have changed categories today.

Usual graphs and labels for all five categories today. All five graphs are in the usual styles. The main graph is on the regular scale, from zero to 200 new cases diagnosed per day per million people. Where there are smaller inset graphs, they are on the “Qatar scale,” which runs from zero to 700 cases per million people. Each country gets a unique color in each graph (although the colors can repeat across graphs). Line labels show the name of the region, and also the mortality rate (cumulative deaths per million people) in the region – cumulative because the dead stay dead. The thickness of the lines and the size of the labels depend on the cumulative case fatality rate – the number of people who died divided by the number diagnosed.

Regions where COVID-19 was quickly contained

Regions where COVID-19 was quickly contained

No changes to this category. In fact, New Zealand has now gone 100 days without community transmission (virus transmission from one person to another within New Zealand). That is an incredible achievement.

Regions where COVID-19 is currently under control(-ish)

Regions where COVID-19 is currently under control

The last two weeks have seen a worrying increase in cases in France, but not enough to move France out of the under control category.

Regions moving in the right direction(-ish)

Regions where cases are decreasing

Welcome to getting better, Serbia!

And the rate of new cases reported in Sweden continues at a level lower than Russia and Saudi Arabia. Sweden took a risk by having less restrictive social distancing laws than nearby countries. Is the risk paying off? Not so far – even though cases are low right now, the death rate in Sweden is far higher than other countries. Sweden is playing the long game, betting that in the long run their approach will be more sustainable and deaths in other countries will inevitably catch up. We’ll see. The long game is the only game worth playing.

Regions experiencing a second wave of COVID-19 cases

Regions experiencing a second wave of COVID-19

This is the graph where Singapore goes, where cases have been increasing since early July. Also, it’s been a while since we’ve checked in with Iran, where cases started to increase in late April but have stayed at a fairly low level since.

Regions where the first wave of COVID-19 continues to get worse

Regions where things are getting worse

Reported cases continue to decrease in Florida.

Want to give it a try? Please do! Here is version 7.2 of my spreadsheet.

Pandemic updates tomorrow, and every day until the pandemic ends or I do.