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.

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.

Ranking the Red Sox Prospects

My regular readers will know that I am a fan of the Boston Red Sox, an American baseball team located in Boston, Massachusetts. I would consider myself a bit more involved than a casual fan in that I keep tabs on the team’s prospects.

For those unfamiliar with baseball, the sport works by keeping development pipelines of young talent fed through what we call a farm system. In essence a number of teams owned or contractually linked to the Major League team develop young players until they are ready to debut at the sport’s highest level.

Very few of total number of players in the system will ever get called up to “the Show”. In fact, in the history of the sport only 20,000 men have reached that level. Most of the rest will peak somewhere in the Minor Leagues. Most that reach the Majors will have been at some point prospects. And so to keep tabs on your team’s prospects and farm system sets one apart, in my mind, from the casual fan who simply knows a few of the team’s star players and enjoys a hot dog and a pint of beer at the stadium a few times a summer.

Red Sox fans are fortunate to have a website dedicated to coverage of Boston’s farm system, SoxProspects.com. They rank the system’s Top 60 prospects using their own methodology and research and publish the list online for fans like myself to enjoy.

Last week they updated their rankings. Long story short, the pandemic has impacted baseball and the development of young players. Consequently, the rankings changed significantly. What I really wanted to see was a visualisation of all the changes. So I took it upon myself to do just that using their data.

Hopefully we get a good player or two out of this

Now, if you also happen to be a Red Sox fan, I highly recommend their site. It’s fantastic. Normally I would take the train up to Trenton and see the Portland affiliate when it played there, but the Trenton team no longer exists. I’m not sure when I’ll get to see a Red Sox minor league team again. But hopefully sometime soon, because there look to be some good players coming up.

So I’ll be looking forward to, hopefully, a good run of contending teams in the coming years.

Credit for the piece is mine.


Happy Friday, all. We made it through another week of Covid, vaccinations, asteroids, and all that pleasant stuff. So let’s end with an upbeat note.

Over on YouTube there’s a channel I have long enjoyed, CCP Grey, who creates videos about, well lots of things, but sometimes really interesting historical, geographical, and political topics.

This week he released a video about Tiffany. As in the name Tiffany.

In addition to some great 80s aesthetics, the video touches on a couple of things that particularly interest me.

You see names are an important part of genealogical research. After all, almost all of us have names. (Some infants died without names.) Now in my family, on both my mother’s and father’s side I have a lot of Johns. In fact, I broke a line of five consecutive John Barrys. But occasionally a family will have a rarer or more uncommon name that allows you to trace that individual and therefore his or her family through time and space/place.

Grey tracks the history of the name Tiffany from its possible origin to some reasons for its popularity in the 1980s. And that includes some great graphics like this chart tracking the number of children with the name.

Thinking I need breakfast…

In the screenshot above you can see one thought he has on why the name took off in the latter half of the 20th century after languishing for centuries.

But he also examined the family history of one Tiffany and how that became important in the cultural zeitgeist. And to do so he used a family tree.

Family trees, with so many deaths in infancy

It’s a nine-minute long video and well worth your time.

I think what’s interesting to consider, however, is how this story could be told for many if not most names. There’s a reason they exist and how, by pure happenstance, they survive and get passed down family lines.

Though I have to say I did a quick search in my family tree and I have not a single Tiffany.

Credit for the piece goes to CCP Grey.

The Pandemic of the Unvaccinated

Get your shots.

It’s pretty much that simple. But for just under half the country, it’s not getting through. So I went looking for some data on the breakdown of Covid-19 cases by vaccinated and unvaccinated people.

I found an analysis by the Kaiser Family Foundation (KFF), a non-profit that focuses on health and healthcare issues. They collected the data made available by 24 states—not all states provide a breakdown of breakthrough cases—and what we see across the country is pretty clear. If you want more details on their methodology, I highly recommend you check out their analysis.

Breakthrough cases

In all but Arizona and Alaska, vaccinated people account for less than 4% of Covid-19 cases. In most of these states, it’s less than 2%. For the states that we regularly cover here—Pennsylvania, New Jersey, Delaware, Virginia, and Illinois—we have New Jersey, Delaware, and Virginia represented in the data set.

Delaware leads the three with vaccinated people accounting for just 1% of Covid-19 cases. Virginia is 0.7% and New Jersey is just 0.2%. In other words, in New Jersey almost nobody vaccinated is catching Covid-19 over the observation period.

And when we look at the vaccinated population, we can see what breakthrough events—cases, hospitalisations, and deaths—they are experiencing.

In almost all states, less than 0.5% of vaccinated people are getting Covid-19. Only in Arkansas do we see a number greater than that: 0.54%. In no state do we have more than 0.6% of vaccinated people requiring hospitalisation. And with that number so low, it won’t surprise you that in no state do we have more than 0.01% of vaccinated people dying.

In other words, the rapidly climbing numbers of new cases and slowly rising deaths that we looked at yesterday, that’s almost all in people who haven’t yet gotten vaccinated.

Get your shots.

Credit for the piece is mine.

Covid Update: 9 August

Late last week I provided a brief update on the Covid-19 situation in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. Today I wanted to circle back to my statement that I’d update everyone again early this week. Of course, we had to wait until states began reporting their Monday data to get a better sense of where we are at in terms of new cases and deaths.

Spoiler: nowhere good.

Let’s start, as usual, with new cases.

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

We can see just from the tail end of the charts above that new case growth is accelerating in nearly all five states. Nearly because New Jersey’s growth has remained fairly constant, in other words the number of new people getting infected is not becoming larger each day but remaining relatively flat. That said, compared to 28 July, my last more thorough update, the seven-day average for new cases is still up by 66%.

In the other four states we see accelerating growth, i.e. the number of people infected grows daily. Virginia and Illinois are perhaps in the worst positions. Consider that earlier this spring during the Third Wave, Virginia peaked with a seven-day average of 1615 new cases per day. Yesterday the seven-day average reached 1625. This Fourth Wave is making more people sick now than they were in the spring. Illinois is not yet at the peak of its Third Wave, 3390 new cases per day, but yesterday the Land of Lincoln reached 2713. It’s not far from that ugly benchmark. Can Illinois’ seven-day average see an increase of about 600 new cases per day in a week? Consider that one week ago the average was at 1914. That’s an 800-new case increase. I would expect that if my next update is next Tuesday we will find Illinois in a worse position now than it was in this past spring.

What about the last two states of the tri-state area? Fortunately—for now—both Pennsylvania and Delaware remain below, roughly by half or so, their springtime peaks during the Third Wave. In part, that’s because—along with New Jersey—the Northeast has some of the highest rates of vaccination. But none of those states are near the levels we would need for herd immunity, especially given the increased transmissibility of the Delta variant.

In Pennsylvania the seven-day average for new cases is now just shy of 1500 new cases per day. Interestingly, if we halve the Monday data that includes both Sunday and Monday the daily numbers of new cases have declined for five consecutive days. I wouldn’t expect that trend to continue given the rampancy with which Delta is spreading throughout the Commonwealth, but that would be the signal in the data we would be looking for when this Fourth Wave breaks.

Delaware reports much the same. Cases are significantly up, but now so much so as to outpace the Third Wave. The First State’s seven-day average now sits at 185 new cases per day, but for the past four days the daily number has exceeded 200. Unlike Pennsylvania, that’s not the signal we would want to see to give us a sense the wave might be breaking.

What about deaths? Last week I mentioned we were seeing those numbers begin to creep back up despite falling during the initial weeks of the Delta wave.

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

The tail ends here, with the exception of Illinois, are far harder to see. In Illinois, on 28 July the seven-day average for deaths bottomed out at 4 deaths per day. Deaths have climbed ever since, tripling to 12 deaths per day. Prior to yesterday, the state had seen double-digit daily deaths for five consecutive days for the first time since early June. These are signs that deaths are heading in the wrong direction. But if we want to try and find a glimmer of hope, those deaths started at 18 on 4 August, but have dropped each day landing at 10 on 8 August and just 6 yesterday. Fingers crossed?

In the remaining states the picture is similar in that deaths are rising, but not nearly as badly as they are in Illinois. In Illinois the death rate tripled, but to be fair it also did so in Delaware. Though that meant climbing from 0.1 to 0.3. In the states where we are seeing deaths from Covid-19, the rates have not even doubled. Pennsylvania and New Jersey are the two closest to hitting that grim number. Their seven-day averages of 3.6 and 3.7, respectively, have reached only 6.6 and 6.4, respectively. Certainly not good, but perhaps we can be cautiously optimistic given the states’ relatively high rate of vaccination.

In Virginia we have seen the death rate climb from an average of 4.4 per day, nearly the same rate as Illinois, which has a lower overall rate of vaccination, to only 5.6 deaths per day as of yesterday.

It is important to note that vaccinations are doing a good job at keeping the vaccinated from needing to go to hospital or even dying. The phrase “pandemic of the unvaccinated” is very accurate. Whilst the vaccinated can become infected, most suffer very mild symptoms or are asymptomatic. The reason for masking is that the Delta variant can infect the vaccinated to such a degree that, whilst not sick, they can infect the unvaccinated.

If you have not been vaccinated yet, it is critical that you do so. They are safe. They are effective. And they are free. There are only a few valid reasons for not receiving the vaccination. And “not wanting it” or “not needing it” or “not trusting the government” or “not sure whence the virus came” are not valid excuses.