Sunday’s Covid Numbers

I do not want this blog to become a permanent Covid-19 data site. So in my push to resume posting last week, I tried to keep to from posting the numbers and instead focused on discussing how the data is displayed.

But I hear from quite a few people via comments, DMs, emails, and text messages that they find the graphics I produce helpful. So on the blog, I’m going to try posting just one set of graphics per week. Will it always be Monday? I don’t know. On the one hand, new week, new data. But on the other, weekend numbers tend to be lower than the rest of the week and could make it seem like, yay, the numbers are starting to go down especially if you only come to my blog and only see this data once a week.

Daily cases and their rolling average for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois
Daily new cases
Daily new deaths and their rolling average for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois.
Daily new deaths

So yeah, we’ll see how this goes. And I’ll try to keep Tuesday–Friday to discussing the world of data visualisation, although in these days, a good chunk of it will likely revolve around Covid.

Credit for these graphics is mine.

Corona’s Moment in Your Life

Earlier this week I was on the social medias when I came across a graphic some people were sharing that was meant to be inspirational. It had a giant circle and then a small black pixel that represented “this moment”. Of course, how you define the moment is entirely subjective.

But it made me wonder, if we looked at the coronavirus Covid-19 pandemic as a moment in our lives, how big of a moment is it? Well, I went to the CDC to get a sense of the average life expectancy of an American and then I got the fraction of that lifespan that is the last six months. And, well take a look.

Corona in your lifespan
A not so insignificant span of time

As you can see, the Covid-19 pandemic is more than just a pixel. It’s a significant moment, and of course the pandemic is ongoing. There are new concerns that the 2020 Olympics, now postponed to 2021, may not happen in 2021.

That dot represents graduations, weddings, funerals, birthdays, anniversaries, holidays, opportunities for education, career advancement, life goals all delayed or in some cases missed and never to return.

And while the rest of the world shows some signs of improvement, for my American audience, things are going from bad to worse.

So Happy Friday, everyone.

The Vaxx Path

Today we look at a wee graphic from the BBC examining the current state of Covid-19 vaccines. None have been approved, but 163 are on the path to approval.

The vaxx path

This falls into the category of not everything has to be super complex. Each vaccine is shown as a discrete unit, a small square. For me in this instance this works better than a bar chart showing the total number per each phase. It highlights how each vaccine is a distinct unit and that it can move from one section down to the next. (Although I suppose if it fails a phase it can also be removed entirely.)

And if you want another reason why a nationalist, isolationist foreign policy that bashes foreign countries is not great…none of the Phase 3 candidates, closest to approval, are from an American company or institution.

Credit for the piece goes to the BBC graphics department.

Red Sox Starting Rotation: A Dumpster Fire in a Dumpster Fire Year

Baseball for the Red Sox starts on Friday. Am I glad baseball is back? Yes?

I love the sport and will be glad that it’s back on the air to give me something to watch. But the But the way it’s being done boggles the mind. Here today I don’t want to get into the Covid, health, and labour relations aspect of the game. But, as the title suggests, I want to look at a graphic that looks at just how bad the Red Sox could be this (shortened) year. And over at FiveThirtyEight, they created a model to evaluate teams’ starting rotations on an ongoing basis.

The Red Sox are just bad.
Look at the Red Sox, one of the worst in baseball.

Form wise, this isn’t too difficult than what we looked at yesterday. It’s a dot plot with the dots representing individual pitchers. The size of the dots represents their number of total starts. This is an important metric in their model, but as we all know size is a difficult attribute for people to compare and I’m not entirely convinced it’s working here. Some dots are clearly smaller than others, but for most it’s difficult for me to clearly tell.

Colour is just tied to the colour of the teams. Necessary? Not at all. Because the teams are not compared on the same plot, they could all be the same colour. If, however, an eventual addition were made that plot the day’s matchups on one line, then colour would be very much appropriate.

I like the subtle addition of “Better” at the top of the plots to help the user understand the constructed metric. Otherwise the numbers are just that, numbers that don’t mean anything.

Overall a solid piece. And it does a great job of showing just how awful the Red Sox starting rotation is going to be. Because I know who Nate Eovaldi is. And I’ve heard of Martin Perez. Ryan Weber I only know through largely pitching in relief last year. And after that? Well, not on this graphic, but we have Eduardo Rodriguez who had corona and, while he has recovered, nobody knows how that will impact people in sports. There’s somebody named Hall who I have never heard of. Then we have Brian Johnson, a root for the guy story of beating the odds to reach the Major Leagues but who has been inconsistent. Then…it is literally a list of relief pitchers.

We dumped the salary of Mookie Betts and David Price and all we got was basically a tee-shirt saying “We still need a pitcher or three”.

Credit for the piece goes to Jay Boice.

Consumer Payment Methods During the Corona Times

Okay, so we’re going to post some more of my work today, but it’s not about cases and deaths. Instead, I took some data produced by my colleagues and thought that it could do for a small transformation from a table into a chart. The original table can be found in their report on consumer payment options during the Covid-19 pandemic.

After setting the kettle on for some tea this morning we started on their Table 1. Thirty minutes later and a cup of Irish Breakfast consumed, I had transformed it into this:

Obviously I changed the language/title a little bit. But the original was too long and didn’t fit. Also this is my blog, so my rules. The visualisation improves upon the table in a number of ways, but tables do have their place. Tables are great for organising information. Find a column header and a row header and you can get any specific data point. But, if you want to make a comparison between two data points or several of them, a chart is the way to go. Now, you may lose some precision. For example, do I know to the decimal point or to the tenths even what one of those dots represents? Nope. But at a glance, can I see which dots are below the overall respondents? Yep. It’s abundantly clear that those earning less than $40,000 per year have a greater availability of debit cards than the other groups shown.

And after all, I couldn’t have made this graphic without that table.

Full disclosure, as alluded to above, I work at the Federal Reserve Bank of Philadelphia. But I had nothing to do with the data, report, or presentation thereof.

Credit for the graphic is mine. The data to the folks over at the Consumer Finance Institute.

A Map of Unequal Comparisons

I’ve largely been busy creating and posting content on the Covid pandemic and its impact on the Pennsylvania, New Jersey, and Delaware tristate area along with, by request, both Virginia, and Illinois, my former home. It leaves me very little time for blogging, and I really do not want this site to become a blog of my personal work. That’s why I have a portfolio or my data project sites, after all.

But in posting my Covid datagraphics, I’ve come across variations of this map with all sorts of meme-y, witty captions saying why Canada is doing so much better than the US, why Americans shouldn’t be allowed to travel to Canada, and now why the Blue Jays shouldn’t be allowed to host Major League Baseball games.

Wait just a minute, there…

Well, that map isn’t necessarily wrong, but it’s incredibly misleading.

First, the map comes from the fantastic Johns Hopkins work on Covid-19. (Full disclosure, that’s the data source I use at work to create my work work datagraphics: And their site has a larger and more comprehensive dashboard (still hate that term but it does have sticking power) of which the map is the focal point.

The numbers as of this posting.

You can see the map there in the centre and some tables to the left, some tables to the right, and even a micro table beneath thundering away at the map’s position. I could get into the overall design—maybe I will one of these days—but again, let’s look at that map.

The crux of the argument is that there are a lot of red dots in the United States and very few in Canada. But look at the table in the dashboard on the left. At the very bottom you see three small tabs, Admin 0, Admin 1, and Admin 2. Admin 0 contains all entities at the sovereign state level, e.g. US, Canada, Sweden, Brazil, &c. Admin 1 is the provincial/state level, e.g. Pennsylvania, Illinois, Ontario, Quebec, &c. Admin 2 is the sub-provincial/sub-state level, e.g. Philadelphia County, Cook County, Chester County, Lake County, &c.

Notice anything about my examples? Not all countries have provinces/states, but Canada certainly does. And then at Admin 2, the examples and indeed the data only have US counties and US data. Everything in Canada has been aggregated up to Admin 1. And that is the problem.

The second part to point out is the dot-ness of the map. And to be fair, this is part of a broader problem I have been seeing in data visualisation the last few months. Dots, circles, or markers imply specificity in location. The centre of that object, after all, has to fall on a specific geographic place, a latitude and longitude coordinate. It utterly fails to capture the dimensions and physical size of the geographic unit, which can be critical.

Because not all geographic units are of the same size. We all know Rhode Island as one of the smallest US states. Let’s compare that to Nunavut or Yukon in Canada, massive provinces that spread across the Canadian Arctic. Rhode Island, according to Google, 1212 square kilometres. Nunavut? 808,200.

So now show both states/provinces on a map with one dot and Rhode Island’s will practically cover the state. And it will also be surrounded by and in close proximity to the states or Massachusetts and Connecticut. Nunavut, on the other hand will be a small dot in a massive empty space on a map. But those dots are equal.

Now, combine that with the fact that the Hopkins map is showing data on the US county level. Every single county in the United States gets a red dot. By default, that means the US is covered with red dots. But there is no county-level equivalent data for Canada. Or for Mexico (also seen in the above graphic). And so given we’re only using dots to relate the data, we see wide swaths of empty space, untouched by red dots. And that’s just not true.

Yes, large parts of the Canadian Arctic are devoid of people, but not southern Ontario and Quebec, not the southwestern coast of British Columbia, not the Maritimes.

The Hopkins map should be showing geographic units at the same admin level. By that I mean that when on Admin 0, the map should reflect geographic units of sovereign state level, allowing us to compare the US to Canada directly. But, and for this argument I’m assuming we’re keeping the dots despite their flaws, we only see Admin 0 level data.

Admin 1 shows only provincial level data. Some countries will begin to disappear, because Hopkins does not have the data at that level. But in North America, we still can compare Pennsylvania and Illinois to Ontario and Quebec.

But then at Admin 2, we only see the numerous dots of the United States counties. It’s neither an accurate nor a helpful comparison to contrast Chester County or Will County to the entire province of Ontario and so the map should not allow it. Instead, as the above graphic shows, it creates misconceptions of the true state of the pandemic in the US and Canada.

Credit for the Hopkins dashboard goes to, well, Hopkins.

Corona Curves

It’s Friday. I’d normally say something like we’ve survived this far, but the fact of the matter is that thousands have not. But, still, let’s try to keep it a little light. So here’s something from xkcd about the shape of the various curve potentials for Covid-19.

PA seems to be somewhere around Scenario 2.

Credit for the piece goes to Randall Munroe.

Wednesday’s Covid-19 Data

Here we have the data from Wednesday for Covid-19.

The situation in Pennsylvania
The situation in Pennsylvania

Pennsylvania saw continued spread of the virus. Notably, Monroe County in eastern Pennsylvania passed 1000 cases. It was one of the state’s earliest hotspots. That appears to have been because it was advertised as a corona respite for people from New York, not too far to the east and by then in the grips of their own outbreak.

The situation in New Jersey
The situation in New Jersey

New Jersey grimly passed 5000 deaths Wednesday. And it is on track to pass 100,000 total cases likely Friday or Saturday. Almost 2/3 of these cases are located in North Jersey, with some South Jersey counties still reporting just a few hundred cases and a handful of deaths.

The situation in Delaware
The situation in Delaware

Delaware passed 3000 cases and Kent Co. passed 500. While those don’t read like large numbers, keep in mind the relatively small population of the state.

The situation in Virginia
The situation in Virginia

Virginia has restarted reporting deaths, this time at the county level and not the health district level. What we see is deaths being reported all over the eastern third of the state from DC through Richmond down to Virginia Beach. In the interior counties we are beginning to see the first deaths appear. And in western counties, we still see that the virus has yet to reach some locations, but counties are beginning to report their first cases.

The situation in Illinois
The situation in Illinois

Illinois continues to suffer greatly in the Chicago area, and at levels that dwarf the remainder of the state. However, the downstate counties are beginning to see spikes of their own. Macon and Jefferson Counties each saw increases of 30–40 cases in just 24 hours.

Preview(opens in a new tab)

How about those curves?
How about those curves?

A longer-term look at the states shows how the states diverge in their outbreaks. Pennsylvania looks like it might be forcing the curve downward whereas New Jersey appears to have more plateaued. Earlier I expressed concern about Virginia, which does now appear to have not peaked and continues to see an increasing rate of spread. Then we have Illinois, which may have plateaued, but we need to see if yesterday’s record amount of new cases was a blip or an inflection point. And in Delaware a missing day of records makes it tricker to see what exactly the trend is.

Credit for the piece is mine.

Comparing Covid-19 to Influenza

I want to share a small graphic I made yesterday evening. And I am being charitable with the term graphic. Really it is nothing more than a collection of organised factettes. But I have seen the footage of those protesting the lockdowns in various states, including Pennsylvania.

To be clear, people can have different policy prescriptions to solve the pandemic. For example, the governor of Pennsylvania is considering lifting the lockdown piecemeal once the state overall has sufficient testing and tracing capabilities. Look at the state.

The situation in Pennsylvania
The situation in Pennsylvania

He rightly said that Cameron County, one of the little light purple shapes in the upper left, with its one case for the last 25 days is in a different situation than Philadelphia where cases continue to grow, albeit at a slowing rate. And in the future it is possible that Cameron County could open before Philadelphia. That is a different policy prescription than, say, opening the state all at once.

I don’t think most people enjoy lockdown—I haven’t left my building in 38 days and I cannot wait to leave and go do something. But I recognise that spreading outside these walls we have a deadly pandemic for which we have no vaccine. But then I see people protesting—protesting in a manner that contradicts the guidelines put out by the health officials—and claiming that we should open up because this is nothing worse than the flu.

Well, Covid-19 is not the flu. It is much worse.

This isn't your grandmother's flu. Or anyone else's flu. Because this isn't the flu.
This isn’t your grandmother’s flu. Or anyone else’s flu. Because this isn’t the flu.

Now, those numbers will change because the pandemic is ongoing. But, let’s spitball. Let’s assume those numbers hold. The idea of the shutdowns, lockdowns, and quarantines is to prevent the spread of the virus. For the sake of this thought experiment, let’s just assume, however, that it infects 56 million people, the upper end of the range for this most recent influenza season.

Influenza this year killed as many as 62,000 people after infecting 56 million. Hypothetically, with a mortality rate of 5%, Covid-19 would kill 2,800,000 people.

With a 4% rate that drops to 2,240,000

With a 3% rate that drops to 1,680,000

With a 2% rate that drops to 1,120,000

With a a 1% rate that drops to 560,000

With a 0.5% rate that drops to 280,000

And even at 0.5% that is still far greater than the flu. And so that is why it is so important to keep the number of people infected as low as possible. (And I won’t even get into the surge problems overwhelming hospitals that acts as a force multiplier and is the proximate reason for the lockdowns.)

This is not the flu.

Credit for the piece is mine.

Covid-19 Data from Monday

Monday’s Covid-19 data for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois provided a glimmer of good news, most notably in Pennsylvania. That, however, occurred on the same day as a protest in Harrisburg that could set the state back days if not weeks. More on that below.

The situation in Pennsylvania
The situation in Pennsylvania

Pennsylvania saw fewer than 1000 new cases for the first time since 1 April. The curve here may be doing more than flattening, but it might actually be falling. That is to say the infection rate is decreasing rather than stabilising and holding steady, as it appears to be doing in New Jersey. That said, new cases are appearing sporadically in the rural and less dense areas of the state. Problematically, protestors arrived in Harrisburg to let it be known they are unhappy with the quarantine. Because the rest of us are.

The problem is that it appears a significant percentage of those infected with the virus are asymptomatic carrier, i.e. they are sick, but do not show any symptoms like fever, coughing, difficulty breathing. Critically, they may not appear sick, but they can spread the sickness. And so a gathering of several hundred people in close quarters? Not ideal.

Compare that to a Christian cultish church in Daegu, South Korea. There, an infected parishioner did not heed government calls to isolate and instead attended a church service. The average infected person spreads this virus to two or three people. This congregant? They infected 43 people who then went on to infect other people.

It is quite possible that someone in that Harrisburg protest was an asymptomatic carrier. And given the lack of social distancing, the lack of masks, and the general reckless behaviour, it is quite possible that the rally could be a super-spreading event. But we won’t know for 5–10 days, the apparent incubation period of the virus. Hopefully we dodge the proverbial bullet. But it is quite easy to see how these kinds of protests could lead to surges in infections. And those surges would then force the government to extend its quarantine by weeks thereby defeating the entire point of the protestors.

We get it. Quarantine sucks. But we all have to suck it up.

The situation in New Jersey
The situation in New Jersey

Moving on to New Jersey, where we see continuing evidence of the plateauing of cases. The bulk of the cases remain in the north in the New York suburban counties with the fewest numbers in the counties in South Jersey. However, averages of nearly 3500 new cases daily remains quite high and the death toll of 4377 is likely to continue to climb higher, even if Monday’s 175 new deaths was lower than most days in recent weeks.

The situation in Delaware
The situation in Delaware

Delaware is back to reporting its figures. And in that release, we had Sussex County in the south climb above 1000 total cases. The levels or curves chart at the end will also show how the state might be flattening and stabilising its infection rate, but we will need several days of uninterrupted reporting to make that determination.

The situation in Virginia
The situation in Virginia

Virginia might be worrying. Or it might not be. Cases continue to increase in the big metropolitan counties like Fairfax and Henrico. But, there are still several counties out in the west that remain unaffected. And the curves chart at the end shows how there has not yet been any sort of even a near-exponential growth curve. Instead we just see a steady, slow increase in the number of cases. That in its own way makes it more difficult to see when the curve flattens, because it was already a relatively flat curve.

The situation in Illinois
The situation in Illinois

Illinois continues to be the tale of two states: Chicago vs. everywhere else. The combined Chicago and Cook County have over 20,000 total cases and the surrounding counties add a few thousand more, which gets you over 2/3 of the state’s 31,000 cases. That said, new cases and new fatalities are beginning to pop up in downstate counties.

Looking at the curves
Looking at the curves

Lastly a look at the curves. As I noted above when talking about Pennsylvania, you can clearly see the downward slope of the state’s new cases curve. Compare that to the plateau-like shape of New Jersey. Delaware and Illinois might be approaching a New Jersey-like curves. But I would want to see more data and in Delaware less volatility. But like I said, Virginia is a tricky one to read.

Credit for the pieces is mine.