The United States surpassed 500,000 deaths from Covid-19. On Sunday, in advance of that sobering statistic, the New York Times published a front-page graphic that dominated the layout.
Sunday front page for the New York Times
Usually a front-page graphic will make use of the four-colour process and present richly coloured graphics. This, however, starkly lays out the timeline of deaths in the United States in black and white.
Meaningful graphics do not need to reinvent the wheel. This takes each life lost as a black dot and then, starting at the top in February, plots each day.
Detail of the graphic
The colour here serves as the annotation. The red circle drawing attention to the first reported death. And down the side the tick marks for days. Red lines indicate 50,000 death increments. The labels tell the story, we’ve needed fewer and fewer days to reach each subsequent 50,000 milestone.
As the first wave intensifies in March and April, the space fills with black dots. But as we enter summer and deaths fell, the space lightens. Late autumn and winter bring more death and you can see clearly towards the bottom of the chart, as we approach today, the graphic is nearly solid black.
If we want to look towards a hopeful point in the content, we can see first that it took 17 days then 15 to reach 400,00 deaths and 450,000 deaths, respectively. But it took 19 days to reach 500,000. As a nation we appear to finally be on the downward slope of this wave.
Returning to the piece, it’s a gut punch of simplicity in design.
Credit for the piece goes to Lazaro Gambio, Lauren Leatherby, Bill Marsh, and Andrew Sondern.
Another week, another snowstorm in the Northeast. This winter has been far busier than last, when Philadelphia saw no snow. Unfortunately, whilst people like me enjoy seeing the snow, it’s hampering with testing and vaccination.
Last week we saw some middling signs of improvement, but perhaps partially exaggerated by the closures caused by the storm. When we look back at the last week, despite the impact of a storm later in the week, it’s been a categorically positive week with respect to new cases.
New case curves for PA, NJ, DE, VA, & IL.
After the plateaus of the week before, most notably in the straight line in Pennsylvania, this week we saw the line for the seven-day average resume a sharp trajectory down. That isn’t to say we are seeing a slowdown in that reduction of new cases. Illinois best fits that, but we can see slight flattening of the downward curve also in Delaware and New Jersey. In Illinois’ case, that is still welcome as the state approaches early autumn levels of new case rates. In the remaining states, we still have a little ways to go before we reach those levels.
Deaths, on the other hand, remain a mixed bag of results. Last week we talked about a much improved picture from the week before with Delaware and Virginia in particular exhibiting significantly decreased rates.
Death curves for PA, NJ, DE, VA, & IL.
This week we saw some reversal of fortune in those two same states. In Delaware, the numbers of deaths have ticked back upwards and the seven-day average has made up about a third of the gains we saw. In Virginia, the upward swing can be largely—though not entirely—attributed to a one-day spike in numbers.
Whilst the other three states continued to see gradual improvements, the question over the coming week will be what trends emerge within Delaware and Virginia. Do the deaths increase and the situation worsen? Or will the increases prove a temporary aberration followed by a return to decreasing numbers of new deaths.
Finally with vaccines
The vaccination curves for PA, NJ, DE, VA, & IL.
The story to follow in Pennsylvania will be how distribution sites mistakenly administered second doses as first. 60,000 people awaiting their second dose will now have to wait—though still within the recommended window—for their second dose whilst 50,000 people will now have to wait for their first dose.
Otherwise, we continue to see an uptick in vaccinations. Last week we saw states make significant gains in their fully vaccinated populations. Virginia had passed 4% and Pennsylvania was about to hit the same milestone. This week begins with Virginia at nearly 5.5% and Pennsylvania almost at 5%, sitting on 4.77%. We need to keep in mind that this excludes any new vaccinations from the city, which doesn’t report vaccination data at the weekend. Illinois is now the lagging state at 4.29%.
The middle third of the United States sits under some pretty cold Arctic air, helping to bring frozen precipitation, i.e. snow, to places unfamiliar with it, most notably Texas. I say unfamiliar, but Texas is also negligently unprepared. There are photos circulating the internet of Texarkana, a city straddling the Texas–Arkansas border, of the Arkansas side plowed and safe for travel whilst the Texas side…is not. You also have a deregulated and privatised energy grid, which has seen wholesale prices spike to $9,000 per megawatt hour. (Some companies in the Texan market charge wholesale rates to their customers, so I wouldn’t be surprised to see stories coming out in the next few weeks of excessively large electric bills.)
What’s driving this Texas-scale cold? Why is Arctic air over Dallas? In years past you’ve probably heard the term “polar vortex”. The super simple version is that really cold air spins in a tight upper-atmosphere vortex over the pole, hence the name. But when the vortex weakens, say due to warmer air, it becomes a bit more unstable. When it becomes unstable, a chunk of it might break off and descend south. It doesn’t always descend over continental North America, but when it does… well, since the mid 2010s we’ve been calling it the polar vortex. Keep in mind, that it’s not, it’s a part of an upper-level low whose cold air eventually falls to the surface, but despite my protestations, the name has stuck.
Anyway, it means we presently are witnessing some frigid temperatures across the US and this graphic from the National Weather Service (NWS) highlights just how extreme those temperatures are.
The distance is no surprise here, because in winter one would expect Minneapolis and Miami to exhibit extreme temperature differences. But it’s the scale of this difference that is so dramatic.
I could probably do a whole piece—or several—about the design of NWS graphics, but I’ll just point out that I’d probably lighten the black lines working as state/provincial borders. And while their colours are standardised, I wonder if making a clearer distinction between freezing and above freezing (32ºF in this map) would make some more sense.
Last week we discussed the potential impact of a major nor’easter that struck the East Coast and interrupted testing and vaccination operations in the states we cover: Pennsylvania, New Jersey, Delaware, Virginia, and Illinois (affected by the storm as one of the components moved east across the Midwest).
The possibility of an exaggerated downward trajectory concerned me and that it could be followed with an uptick in new cases and deaths. So a week later, where are we?
New case curves for PA, NJ, DE, VA, & IL.
We can see something in the middle. With the exception of Illinois, which has continued its downward trend for new cases, we saw a brief interruption last week. In some cases, like Pennsylvania, that emerged as a rolling seven-day average that began and ended the workweek with the same exact number. And without a lot of variation during the week, you can see that pattern as the flat line towards the end of the chart. As numbers resumed heading down, you can see that beginning of a downward direction at the line’s very end.
In the remaining states of New Jersey, Delaware, and Virginia we saw brief upticks in the seven-day averages with daily spikes of new cases. None of these upticks came anywhere close enough to be threatening—though any upward tick should be monitored—but they were all significant enough to be seen as the quick, upward pointing jogs in the lines. But as we entered the weekend, those numbers also began to drop again.
Next we look at deaths. Last week I described a muddled picture. Delaware and Virginia had begun to rebound and reach or approach new peaks whilst Pennsylvania and Illinois continued to see steady but significant declines. New Jersey fell somewhere between the two. What about this week?
Death curves for PA, NJ, DE, VA, & IL.
This week is an improved picture. We did see the potential interruption from the storm—Pennsylvania’s death trend evinces the disruption with the same straight line pattern we saw with new cases. But, overall, numbers continue to trend down. Delaware and Virginia show dramatic improvement with steep drops over the last week. And whilst Illinois continues to show steadily declining numbers, New Jersey now falls somewhere near the top of the pile. Its death rate continues to decline very slowly, relative to the other states. But it is heading down.
Finally, a look at vaccinations for Pennsylvania, Virginia, and Illinois.
Last week we talked about how the states all reached at least 2% over the course of the week. Even better news this week.
Vaccination curves for PA, VA, & IL.
Last week we needed approximately one week to climb one percentage point from 1% to 2%. This week in the same one week time period we saw Virginia climb two percentage points from 2% to 4%. Illinois has slowed its vaccination efforts as it’s still in the mid 3% range. And Pennsylvania is tricky. Because the city of Philadelphia does not report its data on the weekend, we have an incomplete picture until after I post this on Mondays. Even though today is Tuesday, yesterday was a holiday so the same pattern holds true. I would suspect, however, the Commonwealth surpasses 4% later today when the new numbers are released or it comes near to reaching that level.
Well, it’s Friday. And in the Northeast that means another snowstorm. In normal times, that would mean a nice half-hour walk to the office wherein my overcoat would likely become covered in snow and my trousers soaked in disgusting, salty, slush water. In other words, I’d need to wash and dry my clothes. But what household appliances should I use?
Thankfully, over at xkcd, Randall Munroe tackled that very problem with this helpful matrix.
Of course my aforementioned scenario is entirely moot, because like so many of you, I haven’t seen my office nor really left my flat in 11 months now. But here’s looking at you vaccines.
Yesterday, one year to the day the Boston Red Sox traded Mookie Betts to the Los Angeles Dodgers, the Red Sox made another big trade, sending Andrew Benintendi, their starting left fielder, to the Kansas City Royals as part of another three-team trade—last year’s three-team part fell apart, but initially involved Boston receiving a quality reliever from the Minnesota Twins.
In this year’s trade, the Red Sox receive an outfielder, Franchy Cordero, from the Royals and a pitcher, Josh Winckowski, from the New York Mets. Boston is sending $2.8 million to Kansas City to help defray the costs. Using data from Baseball Trade Value, we can make a quick graphic to show how this trade shakes out for the teams involved.
How the trade looks, with incomplete data
At first glance, we see that the Red Sox and the Royals are giving up more than they are receiving in value. The Mets look like the clear winner here, by a long shot.
And it could end up that way this time next year.
But, there is one enormous question mark—or maybe three. The Red Sox are also acquiring one player to be named later from the Mets and two from the Royals. Players to be named later are usually not the high end of prospects, but instead of low to middle value. And what appears likely in this case is that the Red Sox will be presented lists of players from both teams and Boston can choose which ones they like. The key here is that this could take a few months to sort out, because Boston wants to see how these players perform in the minor leagues. In 2020, there was no minor league season and so teams have very little to no information on players, which makes it nigh impossible to accurately assess their skill sets.
And so yes, we can make graphics like this and talk about how the Red Sox lost this trade. But in reality, we’ll need to wait a few months to see the last three players of the deal to see how badly—or how well—Boston does in the end.
No two rivers are the same, though they certainly can be similar. Rivers have their own ecosystems and when I was at school, I learned of the different classifications of rivers by the colour of their water: black, white, and clear. Broadly speaking, that just means the amount of sediment dissolved in the river’s water. Black colours appear when slow moving water has absorbed lots from its environment, think swamps. White waters resemble tea or coffee with added milk or cream. This happens when sediments enter and dissolved into the water. Clear water is that, relatively clear and free of sediment.
But a team of scientists at University of North Carolina at Chapel Hill (UNC Chapel Hill) recently released some work where they used shifts in blue to yellow and green to help classify rivers. Their classification differs, but broadly can point to a change from healthy (blue) to unhealthy (yellow and green). The novelty in their work, however, focuses on using satellite imagery to capture the colour of rivers and their evolution since the mid 1980s.
A look at the broader lower-48 of the United States
They published their findings as an interactive application driven primarily by a clickable map. Clearly not all rivers are available, but a large number are, and you can see some obvious patterns at a national scale—their work excludes Alaska and Hawaii. If blue represents healthy rivers, we see healthy rivers in New England and the Pacific Northwest with a host of green rivers in the Mid-Atlantic and Upper Midwest with yellow in the Mississippi basin and southeast.
I wanted to look at Pennsylvania a bit more specifically given my familiarity with the Commonwealth and zoomed in a bit on the map.
The colour of Pennsylvania’s rivers
You can see that using that above scale, Pennsylvania’s rivers are in okay, not great state. Some of the upper stretches of the Delaware and Susquehanna Rivers are coloured blue, but we mostly see a lot of green.
To the right of the map, the designers placed three smaller charts driven by the user’s selection of river. Let’s take a look at the Juniata River as an example—my grandfather grew up living alongside a tributary that emptied into the Frankstown Branch just a short walk from his house.
A look at the Schuylkill River south of the Fairmount Water Works
We can see that the chart on the upper right shows the colour shift over the decades for that observed section of the river. The legend provides the information that the section of the river has shifting blue—gotten healthier—and then below it looks for any seasonal changes. Here the chart is grey, indicating the system lacks enough data for a clear trend. This examines the short changes we might see in a river based on seasonal effects like rainy season, dry season, and human-driven effects—perhaps we pollute more in the spring and then use rivers recreationally in the summer.
Finally a distribution of the river section’s colour, all in wavelengths of light.
My biggest critique here would be the wavelengths. Users likely will not the colour spectrum by wavelength, and adding some labels like blue, yellow, and green could go a long ways to help users understand at what they are looking.
Overall, though, this is a really fascinating project.
I missed last week’s posting on an update to Covid-19. Two weeks on from the last post, things in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois continue to improve, albeit with a few fits and starts. But the downward trend nonetheless can be seen in the new cases charts.
Consider that in the charts from two weeks ago, we saw downward slopes, but a look at the charts in the two weeks hence shows some blips.
Another thing to keep in mind is that a major snowstorm disrupted testing and vaccinating operations in the northeastern states of Pennsylvania, New Jersey, and Delaware. The storm, which also hit northern Illinois and Virginia, also likely impacted those states but to lesser degrees.
New cases curves for PA, NJ, DE, VA, & IL.
That means the downward trends in new cases could be slightly exaggerated in those states. Consequently, rebounds next week should be taken with a grain of salt. Indeed, Sunday’s data releases from the tri-state area were greater than we might normally see with weekend data.
When we at deaths, however, we see a more muddled picture.
Death curves in PA, NJ, DE, VA, & IL.
In states like Delaware and Virginia, the average death rate is now higher than it was two weeks ago. In New Jersey, the rate is down slightly, but after two weeks of it being largely up and so all in all, largely a wash. Instead, it’s only in Pennsylvania and Illinois where we any real improvements in the average death rate. Both states are down and look to continue heading down.
Finally, we look at vaccinations and the percent of state populations that have been fully vaccinated.
The fully vaccinated percentage of the populations of PA, VA, & IL.
Two weeks ago, Pennsylvania and Illinois had just reached 1%. Neither New Jersey nor Delaware is reporting similar data, so both those states remain outside our consideration set. But, all three remaining states—Pennsylvania, Virginia, and Illinois—are now over 2%. Pennsylvania reports at least 2.5%—the city of Philadelphia reports separately from the statewide Department of Health, but does not update its figures at the weekend and so is likely higher. Both Virginia and Illinois have reached 2.3% full vaccination.
Last Friday I shared an xkcd post about the relative smoothness of the Earth. This week he posted an illustration but a slightly different scale. You can see more of Earth’s jagged edges.
Gotta love the Star Trek reference. I’m betting he used the length of the Kelvin timeline Enterprise, which I personally dislike, as it’s significantly larger than the prime timeline Enterprise of Shatner and Nimoy.
With Covid-19, one of the big challenges we face is the rapid mutations in the viral genetic code that have produced several beneficial—from the virus’ standpoint—adaptations. Several days ago the New York Times published a nice, illustrated piece that showed just what these mutations look like.
Of course, these were not just nice illustrations of protein molecules, but the screenshot below is of the code itself and you can see how just a few alterations can produce subtle, but impactful, effects.
In a biological sense, these mutations are nothing new. In fact, humanity wouldn’t be humanity but for mutations. Rather we are seeing evolution play out in front of our eyes—albeit eyes locked in the same household for nearly a year now—as the virus evolves adaptations better suited to spreading and surviving in a host population.
The piece includes several illustrations, but begins with an overall, simplified diagram of the virus and where its genetic code lies. And then breaks that code down similar to a stacked bar chart.
Designers identify where in the code the different mutations occur and the type of mutation. Later on in the piece we see a map of where this particular variant can be found.
I might come back to that map later, so I won’t comment too much on it here.
But I think this piece does a great job of showcasing just what we mean when we talk about virus mutations. It’s really just a beneficial slip up in the genetic alphabet.
Credit for the piece goes to Jonathan Corum and Carl Zimmer.
