Big Beer

A few weeks back, a good friend of mine sent me this graphic from Statista that detailed the global beer industry. It showed how many of the world’s biggest brands are, in fact, owned by just a few of the biggest companies. This isn’t exactly news to either my friend or me, because we both worked in market research in our past lives, but I wanted to talk about this particular chart.

Not included, your home brew

At first glance we have a tree map, where the area of each “squarified” shape represents, usually, the share of the total. In this case, the share of global beer production in millions of hectolitres. Nothing too crazy there.

Next, colour often will represent another variable, for market share you might often see greens or blues to red that represent the recent historical growth or forecast future growth of that particular brand, company, or market. Here, however, is where the chart begins to breakdown. Colour does not appear to encode any meaningful data. It could have been used to encode data about region of origin for the parent company. Imagine blue represented European companies, red Asian, and yellow American. We would still have a similarly coloured map, sans purple and green,

But we also need to look at the data the chart communicates. We have the production in hectolitres, or the shape of the rectangle. But what about that little rectangle in the lower right corner? Is that supposed to be a different measurement or is it merely a label? Because if it’s a label, we need to compare it to the circles in the upper right. Those are labels, but they change in size whereas the rectangles change only in order to fit the number.

And what about those circles? They represent the share of total beer production. In other words the squares represent the number of hectolitres produced and the circles represent the share of hectolitres produced. Two sides of the same coin. Because we can plot this as a simple scatter plot and see that we’re really just looking at the same data.

Not the most interesting scatter plot I’ve ever seen…

We can see that there’s a pretty apparent connection between the volume of beer produced and the share of volume produced—as one would (hopefully) expect. The chart doesn’t really tell us too much other than that there are really three tiers in the Big Six of Breweries. AB Inbev is in own top tier and Heineken is a second separate tier. But Carlsberg and China Resources Snow Breweries are very competitive and then just behind them are Molson Coors and Tsingtao. But those could all be grouped into a third tier.

Another way to look at this would be to disaggregate the scatter plot into two separate bar charts.

And now to the bars…

You can see the pattern in terms of the shapes of the bars and the resulting three tiers is broadly the same. You can also see how we don’t need colour to differentiate between any of these breweries, nor does the original graphic. We could layer on additional data and information, but the original designers opted not to do that.

But I find that the big glaring miss is that the article makes the point despite the boom in craft beer in recent years, American craft beer is still a very small fraction of global beer production. The text cites a figure that isn’t included in the graphic, probably because they come from two different sources. But if we could do a bit more research we could probably fit American craft breweries into the data set and we’d get a resultant chart like this.

A better bar…

This more clearly makes the point that American craft beer is a fraction of global beer production. But it still isn’t a great chart, because it’s looking at global beer production. Instead, I would want to be able to see the share of craft brewery production in the United States.

How has that changed over the last decade? How dominant are these six big beer companies in the American market? Has that share been falling or rising? Has it been stable?

Well, I went to the original source and pulled down the data table for the Top 40 brewers. I took the Top 15 in beer production, all above 1% share in 2020, and then plotted that against the change in their beer production from 2019 to 2020. I added a benchmark of global beer production—down nearly 5% in the pandemic year—and then coloured the dots by the region of origin. (San Miguel might not seem to fit in Asia by name, but it’s from the Philippines.)

Now I can use a good bar.

What mine does not do, because I couldn’t find a good (and convenient) source is what top brands belong to which parent companies. That’s probably buried in a report somewhere. But whilst market share data and analysis used to be my job, as I alluded to in the opening, it is no longer and I’ve got to get (virtually) to my day job.

Credit to the original goes to Felix Richter.

Credit for my take goes to me.

What’s in a Corporate Name?

Last Thursday I wrote about the Wagner Group, an off-the-books semi-private army the Kremlin uses wage war where plausible deniability is desired. During that piece I mentioned Blackwater, one of the more infamous American private security contractor firms.

The day before I had seen a tweet, this tweet, where Samantha Stokes created a matrix to help people remember just what Blackwater did, as compared to Blackstone.

Bridgewater buying Bridgestone whose tires were shot out by Blackwater bought by Blackrock.

Credit for the piece goes to Samantha Stokes.

If You Can’t Stand the Heat, Cut Your Carbon Emissions Pt. II

A few weeks ago I wrote about the United Nation’s Intergovernmental Panel on Climate Change (IPCC) latest report on climate change, which synthesised the last several years’ data. If you didn’t see that post, suffice it to say things are bad and getting worse. At the time I said I wanted to return to talk about a few more graphics in the release. Well, here we are.

In this piece we have a map, three technically. In a set of small multiples, the report’s designers show the observed change, i.e. what’s happening today, and the degree of scientific consensus on whether humans are causing it.

It’s gotten hotter and wetter here in eastern North America

What I like about this is that, first, improved data and accuracy allows for sub-continental breakdowns of climate change’s impacts. That breakdown allowed the designers to use a tilemap consisting of hexagons to map those changes.

Since we don’t look at the world in this kind of way, the page also includes a generous note where it defines all these acronyms. Of course even with those, it still doesn’t look super accurate—and that is fine, because that’s the point—so little strokes outside clusters of hexagons are labelled to further help the reader identify the geographic regions. I really like this part.

I also like how little dots represent the degree of confidence. The hexagons give enough space to include dots and labels while still allowing the colours to shine. These are really nice.

But then we get to colour, the one part of this graphic with which I’m not totally thrilled. The first map looks at temperature, specifically heat extremes. Red means increase in heat extremes and blue means decrease. Fair enough. Hatched pattern means there is low consensus and medium grey means there’s little data. I like it.

Moving to the second map we look at heavy precipitation. Green means an increase and yellow a decrease. Hatched and medium grey both mean the same as before. I like this too. Sure, with clear titling you could still use the same colours as the first map, but I’ll buy if you’re selling you want visual distinction from the red–blue map above.

Then we get to the third map and now we’re looking at drought. Hatched and grey mean the same. Good. But now we have green and yellow, the same green and yellow as the second map. Okay…but I thought the second map showed we need a visual distinction from the first? But what makes it really difficult is that in this third map we invert the meaning of green and yellow. Green now means a decrease in drought and yellow an increase.

I can get that a decrease in drought means green fields and an increase in drought means dead and dying fields, yellow or brown. And sure, red and blue relate to hot and cold. But the problem is that we have the exact same colours meaning the opposite things when it comes to precipitation.

Why not use two other colours for precipitation? You wouldn’t want to use blue, because you’re using blue in the first map. But what about purple and orange, like I often do here on Coffeespoons? This is why I don’t think the designers needed to switch up the colours from map to map. Pick a less relational colour palette, say purple and orange, and colour all three maps with purple being an increase and orange being a decrease.

Colour is my big knock on these graphics, which unfortunately could otherwise have been particularly strong. Of course, I can’t blame designers for going with red and blue for hot and cold temperatures. I’ve had the same request in my career. But it doesn’t make reading these charts any easier.

Credit for the piece goes to the IPCC graphics team.

Sankey Shows Starters Sticking with Sticky Stuff

I spent way more time trying to craft that title than I’d like to admit. Headline writing is not easy.

Quick little piece today about Sankey diagrams. I love them. You often see them described as flow diagrams—this piece is in the article we’ll get to shortly—but they are more of a subset within a flow diagram. What sets Sankeys apart is their use of proportional strokes or widths of the directional arrows to indicate share of movement.

The graphic in question comes from an article about Major League Baseball’s (MLB’s) problem with “sticky stuff”. For the unfamiliar, sticky stuff is a broad term for foreign substances pitchers put on their fingers to provide better grip on the baseball. A better grip makes it easier to create movement like sliding and sinking in a pitch there therefore makes it harder for a hitter to hit it. Back when I was a wannabe pitcher, it was spitballs and scuff balls. Now professionals use things like Spider Tack. These are substances that allow you to put the ball in the palm of your hand, then turn your hand over to face the ground and not have the ball fall out of your hand.

So the graphic looks at starting pitchers and how their spin rate, the quantifiable measure impacted by sticky stuff, of their fastballs has changed since MLB instituted a ban on sticky stuff. (It had actually long been in place, see spitballs for example, but had rarely been enforced.)

Showing a small number of pitchers have managed to increased their fastball spin rates

This graphic explores how 223 pitchers saw their spin rates change in the first two months after the change in policy was announced to the nearly month after that period.

Sankeys use proportional width not just to show movement from category to category but the important element of what share of which category moves to which category. For example, we can see a little less than half of starting pitchers saw their spin rates stay the same after the policy change and another almost equal group saw their spin rates decrease. That’s probably a sign they were using sticky stuff and stopped lest they get caught.

But we can then see of that group, maybe 1/6 then saw their spin rates increase again over the last month. That could be a sign that they have found a way to evade the ban. Though it could also be they’ve found new ways of gripping or throwing the baseball. Spin rate alone does not prove sticky stuff usage.

Similarly, we can see that in the group that maintained their spin rate, a small group has found a way to increase it. Finally, a small fraction of the original 223 saw their spin rates increase and a fraction of that group has seen their spin rates increase even further.

This was just a really nice graphic to see in an article from the Athletic about sticky stuff and its potential return.

Credit for the piece goes to Max Bay.

Covid Update: 23 August

Last week I mentioned how there was some initial evidence showing the rapid, near-exponential spread of the virus was beginning to slow down. One week later, where are we?

The good news is that those initial signals do appear to be true, i.e. not noise. You can see it if you look at the very end of the charts for all but Virginia.

New case curves for PA, NJ, DE, VA, & IL.

We can see the slowing spread rate most easily in Illinois and New Jersey. The shape of the curve now resembles more like the top of a hill rather than the beginning of a roller coaster. To be clear, this doesn’t mean Covid-19 is no longer spread—that is not the case at all. Rather, just the speed at which people are spreading the virus has slowed from that initial rapid acceleration.

In the last week, however, despite the good news for Illinois, we can also see that this fourth wave, driven by the Delta variant, has now exceeded the third wave we saw earlier this spring. Virginia still remains the only other state joining Illinois in that auspicious category, but Delaware is edging ever closer.

However, Delaware as well as Pennsylvania can both join Illinois and New Jersey in seeing slowing rates, though it’s not nearly as evident as in the other two states. Delaware continues to approach its third wave peak.

Virginia is the one state where we might just now be seeing the beginning of a slowdown. Though it’s probably the hardest state in which to see it. Yesterday, after a weekend of no data updates, the state reported over 7,000 new cases. That’s bad. But jumping from 5,900 new cases last Monday to yesterday’s 7,100 is comparatively good. Compare Monday to Monday, four weeks ago the increase was 91%. Three weeks ago it was 88.8%. Three weeks ago it fell to 30.9%. And then two weeks ago it was 26.8%. Yesterday’s increase was only 20%. Again, not great, but that’s a slowdown.

The hope in all five states is that we can begin to actually peak perhaps in early- to mid-September before the seven-day average begins to decline. The question then will be what do things look like as begin to head into flu season, which is when last winter’s deadly surge began in earnest.

What about deaths though?

Death curves for PA, NJ, DE, VA, & IL.

Last week I discussed how we were seeing death rates hold fairly steady with only small increases to the seven-day averages. Unfortunately this past week we saw more significant increases. Nothing too dramatic, to be clear, but increases all the same.

Take Pennsylvania, for example. Two weeks ago we went from an average of 7 deaths per day due to Covid-19 to just 9 last week. But yesterday that number jumped up to 16. Illinois, which had went from 12 to 13, climbed from 13 to 20 as of yesterday.

This is also not terribly surprising. As we are now several weeks into this wave, we would expect to see hospitalisations increase, which we had been seeing, before, sadly, deaths followed suit. We may now be entering that phase where deaths, again, a lagging indicator, begin to rise.

I do not think, however, that we will see numbers near to the extremes we saw this past winter. Even to reach levels we saw this past spring will be difficult. However, I’d be remiss if I didn’t point out that Illinois has reached nearly half its third wave peak number of deaths.

We will need to continue observing these death rates over the coming week to see if they continue to increase. I suspect they will before, like new cases, they begin to slow down before then peaking.

Credit for the piece is mine.

Rarely Shady in Philadelphia

After a rainy weekend in Philadelphia thanks to Hurricane Henri, we are bracing for another heat wave during the middle of this week. Of course when you swelter in the summer, you seek out shade. But as a recent article in the Philadelphia Inquirer pointed out, not all neighbourhoods have the same levels of tree cover, or canopy.

From a graphics standpoint, the article includes a really nice scatter plot that explores the relationship between coverage and median household income. It shows that income correlates best with lack of shade rather than race. But I want to focus on a screenshot of another set of graphics earlier on in the article.

On the other hand, pollen.

I enjoyed this graphic in particular. It starts with a “simple” map of tree coverage in Philadelphia and then overlays city zip codes atop that. Two zip codes in particular receive highlights with bolder and larger type.

Those two zip codes, presumably the minimum and maximum or otherwise broadly representative, then receive call outs directly below. Each includes an enlarged map and then the data points for tree cover, median income, and then Black/Latino percentage of the population.

I don’t think the median income needs to be in bar chart form here, especially given the bars do not line up so that you can easily compare the zip codes. The numbers would work well enough as factettes or perhaps a small dot plot with the zip codes highlighted could work instead.

Additionally, the data labels would be particularly redundant if a small scale were used instead. That would work especially well if the median income were moved to the lowest place in the table and the share charts were consolidated in one graphic. Conceptually, though, I enjoy the deep dive into those two zip codes.

Then I wanted to highlight some great design work on the maps. Note how in particular for Chestnut Hill, 19118, the outline of the zip code is largely in a thicker, black stroke than the rest of the map. At the upper right, however, you have two important roads that define the area and the black stroke breaks at those points so the roads can be clearly and well labelled. The other map does the same thing for two roads, but their breaks are shorter as the roads run perpendicular to the border.

Overall this was just a great piece to read and I thoroughly enjoyed the graphics.

Credit for the piece goes to John Duchneskie.

The Years of the Asterisks

Happy Friday, all. Another week and we made it.

This Friday I want to highlight a graphic from xkcd that, strictly speaking, isn’t really data visualisation, but it does speak to that world because it’s about the underlying data.

And as with the best humour, there’s an element of truth in it.

2020-21, the years of the asterisks

Credit for the piece goes to Randall Munroe.

Little Green Men. Now with Tanks.

In 2014, what became known as little green men invaded Crimea, Ukraine. No, these were not aliens, but what we’ve later learned were unmarked Russian Army soldiers. They routed what little resistance Ukraine mustered in 2021 Crimea is de facto Russian, though de jure it remains Ukrainian.

Following Crimea, insurrections erupted in the Donbas, part of the mainland—yes, Crimea is connected through a thin strip of land, but in many ways it’s effectively not part of the mainland—bordering Russia. We suspect these too also included Russian Army regular, most notably the downing of Malaysian Airlines Flight 17. That civilian passenger airliner, filled with 298 people, was shot down by an SA-11 Gadfly, in use by the Russian Army across the border. But overall, the feeling is that in Ukraine, Russia uses paramilitary forces and private mercenaries, or at worst, soldiers “on holiday” to do Russia’s dirty work.

I covered the invasion of Crimea and the operations in Donbas extensively. Well, extensively for me.

In the years since, we’ve seen the emergence of the Wagner Group, a private mercenary group similar in concept to Blackwater. You have some fighting to do, they’ll do it for cash. But Blackwater, whose name has changed several times over the years, was largely staffed by ex-soldiers and had some infantry weapons to support them. Wagner Group is different.

And to see how different, you need only read this great BBC article that exposes some of the group’s details because of a tablet left behind by a Wagner mercenary. It is a bit of a lengthy read, but it’s well worth it. Wagner has been engaged/hired in Ukraine, Syria, and now Libya where it fights against the UN-backed government in Tripoli.

The data visualisation and information design here is mostly around forms and some illustrations of mines—the blow up and kill/main people kind, not the mineral extraction kind. But what sells the idea that Wagner is really more a shadowy appendage of the Russian state than some rogue private mercenary army are things like this document.

You get a tank, and you get a tank, and you…

It, as the file photo hints, shows that Wagner is requesting a T-72 main battle tank for their operations in Libya. Blackwater committed crimes in foreign countries, but it never operated modern main battle tanks.

I also highlighted two other requisitions contained above the T-72. In purple we have an ask for two BTR-82s. These are more modern versions of the Soviet staple, BTR-80, a wheeled armoured personnel carrier. Then in orange we have a request for one BMP-2. This is a tracked infantry fighting vehicle.

In other words, Wagner is requesting the equipment necessary to field a scaled down version of a modern armoured division with heavy tanks and supporting infantry vehicles, both tracked and wheeled. It also contains requests for 120mm mortars, highlighted by the BBC. These are not things that a private mercenary army would have floating around a warehouse.

For this and other funding-related reasons, Wagner Group is increasingly seen as a part of the Russian government’s, i.e. Putin’s, foreign and security policy apparatus. The Russian state might not be able to be officially involved in Libya or Chad or the Central African Republic, where rumours abound of Russian-speaking mercenaries, but Wagner can because officially it doesn’t exist.

Credit for the piece goes to the BBC.

Out with the New, In with the Old

After twenty years out of power, the Taliban in Afghanistan are back in power as the Afghan government collapsed spectacularly this past weekend. In most provinces and districts, government forces surrendered without firing a shot. And if you’re going to beat an army in the field, you generally need to, you know, fight if you expect to beat them.

I held off on posting anything about the Taliban takeover of Afghanistan simply because it happened so quick. It was not even two months ago when they began their offensive. But whenever I started to prepare a post, things would be drastically different by the next morning.

And so this timeline graphic from the BBC does a good job of capturing the rapid collapse of the Afghan state. It starts in early July with a mixture of blue, orange, and red—we’ll come back to the colours a bit later. Blue represents the Afghan government, red the Taliban, and orange contested areas.

The start of the summer offensive

The graphic includes some controls at the bottom, a play/pause and forward/backward skip buttons. The geographic units are districts, sub-provincial level units that I would imagine are roughly analogous to US counties, but that’s supposition on my part. Additionally the map includes little markers for some of the country’s key cities. Finally in the lower right we have a little scorecard of sorts, showing how many of the nearly 400 districts were in the control of which group.

Skip forward five weeks and the situation could not be more different.

So much for 20 years.

Almost all of Afghanistan is under the control of the Taliban. There’s not a whole lot else to say about that fact. The army largely surrendered without firing a shot. Though some special forces and commando units held out under siege, notably in Kandahar where a commando unit held the airport until after the government fell only to be evacuated to the still-US-held Hamid Karzai International Airport in Kabul.

My personal thoughts, well you can blame Biden and the US for a rushed US exodus that looks bad optically, but the American withdrawal plan, initiated by Trump let’s not forget, counted on the Afghan army actually fighting the Taliban and the government negotiating some kind of settlement with the Taliban. Neither happened. And so the end came far quicker than anyone thought possible.

But we’re here to talk graphics.

In general I like this. I prefer this district-level map to some of the similar province-level maps I have seen, because this gives a more granular view of the situation on the ground. Ideally I would have included a thicker line weight to denote the provinces, but again if it’s one or the other I’d opt for district-level data.

That said, I’d probably have used white lines instead of black. If you look in the east, especially south and east of Kabul, the geographically small areas begin to clump up into a mass of shapes made dark by the black outlines. That black is, of course, darker than the reds, blues, and yellows. If the designers had opted for white or even a light shade of grey, we would enhance the user’s ability to see the district-level data by dropping the borders to the back of the visual hierarchy.

Finally with colours, I’m not sure I understand the rationale behind the red, blue, yellow here. Let’s compare the BBC’s colour choice to that of the Economist. (Initially I was going to focus on the Economist’s graphics, but last minute change of plans.)

Another day, more losses for the government

Here we see a similar scheme: red for the Taliban, blue for the government. But notably the designers coloured the contested areas grey, not yellow. We also have more desaturated colours here, not the bright and vibrant reds, blues, and yellows of the BBC maps above.

First the grey vs. yellow. It depends on what the designers wanted to show. The grey moves the contested districts into the background, focusing the reader’s attention on the (dwindling) number of districts under government control. If the goal is to show where the fighting is occurring, i.e. the contest, the yellow works well as it draws the reader’s attention. But if the goal is to show which parts of the country the Taliban control and which parts the government, the grey works better. It’s a subtle difference, I know, but that’s why it would be important to know the designer’s goal.

I’ll also add that the Economist map here shows the provincial capitals and uses a darker, more saturated red dot to indicate if they’d fallen to the Taliban. Contrast that with the BBC’s simple black dots. We had a subtler story than “Taliban overruns country” in Afghanistan where the Taliban largely did hold the rural, lower populated districts outside the major cities, but that the cities like the aforementioned Kandahar, Herat, Mazar-i-Sharif held out a little bit longer, usually behind commando units or local militia. Personally I would have added a darker, more saturated blue dot for cities like Kabul, which at the time of the Economist’s map, was not under threat.

Then we have the saturation element of the red and blue.

Should the reds be brighter, vibrant and attention grabbing or ought they be lighter and restrained, more muted? It’s actually a fairly complex answer and the answer is ultimately “it depends”. I know that’s the cheap way out, but let me explain in the context of these maps.

Choropleth maps like this, i.e. maps where a geographical unit is coloured based on some kind of data point, in this instance political/military control, are, broadly speaking, comprised of large shapes or blocks of colour. In other words, they are not dot plots or line charts where we have small or thin instances of colour.

Now, I’m certain that in the past you’ve seen a wall or a painting or an advert for something where the artist or designer used a large, vast area of a bright colour, so bright that it hurt your eyes to look at the area. I mean imagine if the walls in your room were painted that bright yellow colour of warning signs or taxis.

That same concept also applies to maps, data visualisation, and design. We use bright colours to draw attention, but ideally do so sparingly. Larger areas or fields of colours often warrant more muted colours, leaving any bright uses to highlight particular areas of attention or concern.

Imagine that the designers wanted to highlight a particular district in the maps above. The Economist’s map is better designed to handle that need, a district could have its red turned to 11, so to speak, to visually separate it from the other red districts. But with the BBC map, that option is largely off the table because the colours are already at 11.

Why do we have bright colours? Well over the years I’ve heard a number of reasons. Clients ask for graphics to be “exciting”, “flashy”, “make it sizzle” because colours like the Economist’s are “boring”, “not sexy”.

The point of good data visualisation, however, is not to make things sexy, exciting, or flashy. Rather the goal is clear communication. And a more restrained palette leaves more options for further clarification. The architect Mies van der Rohe famously said “less is more”. Just as there are different styles of architecture we have different styles of design. And personally my style is of the more restrained variety. Using less leaves room for more.

Note how the Economist’s map is able to layer labels and annotations atop the map. The more muted and desaturated reds, blues, and greys also allow for text and other artwork to layer atop the map but, crucially, still be legible. Imagine trying to read the same sorts of labels on the BBC map. It’s difficult to do, and you know that it is because the BBC designers needed to move the city labels off the map itself in order to make them legible.

Both sets of maps are strong in their own right. But the ultimate loser here is going to be the Afghan people. Though it is pretty clear that this was the ultimate result. There just wasn’t enough support in the broader country to prop up a Western style liberal democracy. Or else somebody would have fought for it.

Credit for the BBC piece goes to the BBC graphics department.

Credit for the Economist piece goes to the Economist graphics department.

Covid Update: 16 August

In last week’s update we looked at how in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois the numbers of new cases of Covid-19 were trending in the wrong direction. This past week they continued to do much the same.

This week I want to begin with New Jersey, because last week I noted how the growth in the number of new cases was holding steady. In other words, the number of new cases, whilst growing, was growing by roughly the same number of cases each day. We contrasted that with the other four states where we witnessed increasing numbers of new cases each day.

New case curves for PA, NJ, DE, VA, & IL.

New Jersey’s continued to see similar growth, fairly flat, though it has increased ever so slightly. And in the other states we continue to see increasing numbers of new cases, but that accelerating growth may be tailing off. That doesn’t mean we are seeing new cases decline—far from it. Instead we are seeing the number of new cases become slightly smaller each day. And if you look ever so closely at the tails of each chart above, you can see how the slope of the line, the seven-day average, is no longer bending upward but is straightening out to a line instead of a curve or, in some cases, maybe even beginning to flatten out as one does as one would approach a peak.

This doesn’t mean we are at the point of seeing this fourth wave peak, but the first indication of such a thing happening would be a slowdown in the numbers of new cases. And so moving forward over the next two weeks or so, we’ll want to see if that continues.

In absolute terms, I mentioned last week that I wouldn’t be surprised if Illinois surpassed its springtime seven-day average peak of 3390 new cases per day. Fortunately, we haven’t yet hit that milestone. We are, however, just under 200 new cases per day away from that. This can speak to that slight slowdown in the numbers of new cases.

We also looked at how in the tri-state area all three states were well below their springtime peaks. That continues to be the case. However, Delaware is nearing that peak.

When we look at deaths, we also see very much the same story as last week.

Death curves for PA, NJ, DE, VA, & IL.

Delaware continues to be the exception where we saw deaths climb by just one. But when we look at the other four states, the concern last week was Illinois where we saw a significant jump in the rate. Fortunately that has slowed down over the past week and deaths climbed from 12 per day to just 13 per day. Similarly, the rate in Pennsylvania and Virginia has also slowed down slightly with 9 and 7 people dying each day in those states, respectively.

The good news is in New Jersey. There the death rate has slowed so much so that the average hasn’t changed. Last week it was 6 per day. As of yesterday’s data update, it’s still sitting on 6.

And we need to mention again that these deaths and the hospitalisations that we don’t track are almost all happening solely in the unvaccinated population. If you haven’t been vaccinated yet, you really need to. Because these vaccines have been proven safe; they’ve been proven effective; and they’re free if you’re worried about cost.

Credit for the piece is mine.