Modelling the Impact of Not Sheltering in Place or Staying at Home

The administration botched the early stages of the COVID-19 pandemic. Only within the last two weeks have states acted to begin enacting dramatic policies aimed at slowing the spread of the virus through their communities. But what policies the federal government has enacted are now threatened by an administration that prioritises the economy and market over the lives of the citizens it leads.  The White House is discussing loosening all the policies of social distancing that health officials and scientists say are necessary to slow the spread of the virus.

This website from CovidActNow.org uses a model to predict the impact state by state of various policies on hospital overcrowding and ultimately deaths. The site opens with a map of the United States showing, broadly, what kind of response each state has followed (understanding things change rapidly these days).

The state of reactions in the United States
The state of reactions in the United States

That also serves as the navigation for a deep dive into those models for that state. Here I have selected my home state of Pennsylvania. It borders New Jersey and New York, two states that revolve, at least in part, around New York City, rapidly becoming the epicentre of the US outbreak, supplanting Seattle and the Pacific Northwest. What would the state face if we allowed things to keep going blithely on? What would happen if we merely socially distance for three months? What if we shelter in place for three months? (Emphasis added by me to show this is a long-term problem.)

Potential outcomes for Pennsylvania
Potential outcomes for Pennsylvania

Turns out that things don’t work out that well if we don’t stay at home, stop travelling, stop socialising. A table below the line charts shows the user how bad things go for the state in a table.

A table of potential outcomes
A table of potential outcomes

As you can see, for Pennsylvania, if we were to continue going on like normal, that would result in the deaths of almost the size of the entire city of Pittsburgh. Imagine if the city of Pittsburgh were suddenly wiped off the state map. That’s the level we are talking about.

Just three months of just social distancing? Well now you’re talking about wiping out just the cities of Allentown and Scranton.

Sheltering in place for three months, statewide? Well, thankfully Pennsylvania has lots of towns around the size of 5000 to choose from. Imagine no more Paoli, or Tyrone. Or maybe a Collegeville or Kutztown. An Oxford or a Media. Pick one of those and wipe it from the map.

Fundamentally the choice comes down to, do you want to restart your economy or do you want to save lives? Saving lives will undoubtedly mean unemployment, shattered 401k plans, bankruptcies, mental health problems, and cities, towns, and industries devastated without a tax base to provide for the necessary services. But, saving those jobs and dollars will means tens if not hundreds of thousands of deaths.

I don’t envy the state executive branches making these decisions.

Pennsylvania has chosen a middle road, if you will. It enacted a stay-at-home policy for seven counties: Allegheny (Pittsburgh); Philadelphia and its suburban counties of Bucks, Chester, Delaware, and Montgomery; and Monroe County. The rest of the state, primarily where the virus has yet to make any real significant appearance or appears to be spreading in the community, is not under the strictest of measures. This site’s model doesn’t account for a partial, statewide stay-at-home, but Pennsylvania’s choice is clearly a far superior one for people who prioritise lives over dollars.

Finally, to the people I have seen from my apartment gathering in parks, partying in outdoor spaces, that I can hear throwing house parties, please stop. If not for you, for the rest of us.

Credit for the piece goes to CovidActNow.org.

The Spread of COVID-19 in Select States

By now we have probably all seen the maps of state coverage of the COVID-19 outbreak. But state level maps only tell part of the story. Not all outbreaks are widespread within states. And so after some requests from family, friends, and colleagues, I’ve been attempting to compile county-level data from the state health departments where those family, friends, and colleagues live. Not surprisingly, most of these states are the Philadelphia and Chicago metro areas, but also Virginia.

These are all images I have posted to Instagram. But the content tells a familiar story. The outbreaks in this early stage are all concentrated in and around the larger, interconnected cities. In Pennsylvania, that means clusters around the large cities of Philadelphia, Pittsburgh, and Harrisburg. In New Jersey they stretch along the Northeast Corridor between New York and Trenton (and along into Philadelphia) and then down into Delaware’s New Castle County, home to the city of Wilmington. And then in Virginia, we see small clusters in Northern Virginia in the DC metro area and also around Richmond and the Williamsburg area. Finally in Illinois we have a big cluster in and around Chicago, but also Springfield and the St. Louis area, whose eastern suburbs include Illinois communities like East St. Louis.

19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19
19 March county wide spread of COVID-19

I have also been taking a more detailed look at the spread in Pennsylvania, because I live there. And I want to see the rapidity with which the outbreak is growing in each county. And for that I moved from a choropleth to a small multiple matrix of line charts, all with the same fixed scale. And, well, it doesn’t look good for southeastern Pennsylvania.

County levels compared
County levels compared

Then last night I also compared the total number of cases in Pennsylvania, New Jersey, Delaware, and Virginia. Most interestingly, Pennsylvania and New Jersey’s outbreaks began just a day apart (at least so far as we know given the limited amount of testing in early March). And those two states have taken dramatically different directions. New Jersey has seen a steep curve doubling less than every two days whereas Pennsylvania has been a bit more gradual, doubling a little less than every three.

State levels since early March
State levels since early March

For those of you who want to continue following along, I will be looking at potential options this coming weekend whilst still recording the data for future graphics.

Credit for the pieces is mine.

The Spread of COVID-19 in Pennsylvania

Over the last several days, along with most of the country, I’ve taken an interest in the spread of the novel coronavirus named COVID-19. Though, to be fair, it’s actually been in the news since early January, though early news reports like this from the Times, simply called it a mysterious new virus.  At the time I thought little of it, because the news out of China was that it did not appear it could spread amongst humans. How did that idea…wait for it…pan out?

Anyway, over the last couple of days I’ve been making some maps for Instagram because people tend to look at a national map and see every nearly state infected when, in reality, there are pockets and clusters within those states. So I started looking at Pennsylvania. And initially, the cluster was along the Delaware River, namely Pennsylvania as well as its upper reaches near the Lehigh Valley and in the far northeast of the state.

But the spread has grown, and fairly quickly, with Montgomery County, a Philadelphia suburb, a hotspot. Consequently, the Pennsylvania governor has shut down all schools across the state and ordered non-essential shops, restaurants, and bars in the counties surrounding Philadelphia—as well as the county containing Pittsburgh—closed.

So 11 days in, here’s where we stand. (To be fair, I looked at including the early numbers out of today, but nothing has really changed, so I’ll wait until the evening figures are released before I update this again.)

Credit is mine. Data is the Pennsylvania Department of Health.

The Vaping Outbreak Spreads

And now with more deaths.

On Friday, Pennsylvania reported its first death from the vaping disease spreading across the country. So I decided I would take a moment to update the map I made a month ago charting the outbreak. Then, the CDC had tallied 450 cases. Now we are at 1080. And whereas last time New England, parts of the deep South, and the Southwest were untouched, now the disease is everywhere but New Hampshire and Alaska.

But we are starting to see a pattern in a clustering of high numbers of cases around Lake Michigan and the Upper Midwest. Though I should point out these bin breakdowns come from the CDC. They did not provide more granular data.

Now with deaths in Pennsylvania.
Now with deaths in Pennsylvania.

Credit for this piece goes to me.

Where the Vaping Illness Is Spreading

Yesterday President Trump announced that the FDA is seeking to implement a ban on flavoured e-cigarettes. Ostensibly this is to combat teen uptake on the habit, but it comes at the same time as an outbreak of respiratory illnesses seemingly linked to vaping. Though, it should be pointed out that preliminary data points to a link to cannabis-infused vaping liquids, not necessarily cigarettes.

Regardless, the day before yesterday, I want to the CDC website to get the data on the outbreak to see if there was a geographic pattern to the outbreak. And, no, not really.

No real clear pattern here
No real clear pattern here

The closest thing that I could argue is the Eastern Seaboard south of New England. But then the deaths are all from the Midwest and westward. So no, in this graphic, there really is no story. I guess you could also say it’s more widespread than not?

Credit for this piece goes to me.

A Very Loud Tube

As all my readers probably know, I love London. And in loving London, I love the Tube and the Oyster Card and all that goes along with Transport for London. But, I have noticed that sometimes when I take the Underground, there are segments where it gets a bit loud, especially with the windows open. The Economist covered this in a recent article where they looked at some data from a London-based design firm that makes noise protective gear. (For purposes of bias, that seems important to mention here.)

The data looks at decibels in a few Underground lines and when the levels reach potentially harmful levels. I took a screenshot of the Bakerloo line, with which I am familiar. (At least from Paddington to Lambeth.) Not surprisingly, there are a few segments that are quite loud.

I definitely recall it being loud
I definitely recall it being loud

I like this graphic—but like I said about bias, I’m biased. The graphic does a good job of using the above the 85-decibel line area fill to show the regions where it gets loud. And in general it works. However, if you look at the beginning of the Bakerloo line noise levels the jumps up in down in noise levels, because they happen so quickly in succession, begin to appear as a solid fill. It masks the importance of those periods where the noise levels are, in fact, potentially dangerous.

I have had to deal with this problem often in my work at the Fed, where some data over decades is available on a weekly basis. One trick that works, besides averaging the data, is thinning out the stroke of the line so the overlaps do not appear so thick. It could make it difficult to read, but it avoids the density issues at the beginning of that chart.

All in all, though, I would love a London-like transport system here in Philly. I’d rather some loud noises than polluting cars on the road.

Credit for the piece goes to the Economist Data Team.

The Ebola Outbreak in the Congo

Ebola, which killed 11,000 people in West Africa in 2014 (which I covered in a couple of different posts), is back and this time ravaging the Congo region, specifically the Democratic Republic of the Congo (DRC). The BBC published an article looking at the outbreak, which at 1,400 deaths is still far short of the West Africa outbreak, but is still very significant.

That's looking like a tenuous border right now…
That’s looking like a tenuous border right now…

The piece uses a small multiples of choropleths for western Congo. The map is effective, using white as the background for the no case districts. However, I wonder, would be more telling if it were cases per month? That would allow the user to see to where the outbreak is spreading as well as getting a sense of if the outbreak is accelerating or decelerating.

The rest of the article features four other graphics. One is a line chart that also looks at cumulative cases and deaths. And again, that makes it more difficult to see if the outbreak is slowing or speeding up. Another is how the virus works and then two are about dealing with the virus in terms of suits and the containment camps. But those are graphics the BBC has previously produced, one of which is in the above links.

Credit for the piece goes to the BBC graphics department.

Put Your Phone Down

This isn’t really a graphic so much as it is an x-ray photograph. But I also can’t get it out of my head. We all know that mobile phones has changed the way we live. But now we have evidence that our use of them is changing us physically. Young people are growing horns or spikes at the back of their skull. Don’t believe, photo:

Cool, but also frightening
Cool, but also frightening

The article in the Washington Post from which I screen captured the image is well worth a read. But I advise you to not do it on a mobile phone.

Credit for the piece goes to the study’s authors.

Abortion by State

In case you did not hear, earlier this week Alabama banned all abortions. And for once, we do not have to add the usual caveat of “except in cases of rape or incest”. In Alabama, even in cases of rape and incest, women will not have the option of having an abortion.

And in Georgia, legislators are debating a bill that will not only strictly limit women’s rights to have an abortion, but will leave them, among other things, liable for criminal charges for travelling out of state to have an abortion.

Consequently, the New York Times created a piece that explores the different abortion bans on a state-by-state basis. It includes several nice graphics including what we increasingly at work called a box map. The map sits above the article and introduces the subject direct from the header that seven states have introduced significant legislation this year. The map highlights those seven states.

We've been calling these box maps. It's growing on me.
We’ve been calling these box maps. It’s growing on me.

The gem, however, is a timeline of sorts that shows when states ban abortion based on how long since a woman’s last period.

There are some crazy shifts leftward in this graphic…
There are some crazy shifts leftward in this graphic…

It does a nice job of segmenting the number of weeks into not trimesters and highlighting the first, which traditionally had been the lower limit for conservative states. It also uses a nice yellow overlay to indicate the traditional limits determined by the Roe v. Wade decision. I may have introduced a nice thin rule to even further segment the first trimester into the first six week period.

We also have a nice calendar-like small multiple series showing states that have introduced but not passed, passed but vetoed, passed, and pending legislation with the intention of completely banning abortion and also completely banning it after six weeks.

Far too many boxes on the right…
Far too many boxes on the right…

This does a nice job of using the coloured boxes to show the states have passed legislation. However, the grey coloured boxes seem a bit disingenuous in that they still represent a topically significant number: states that have introduced legislation. It almost seems as if the grey should be all 50 states, like in the box map, and that these states should be in some different colour. Because the eight or 15 in the 2019 column are a small percentage of all 50 states, but they could—and likely will—have an oversized impact on women’s rights in the year to come.

That said, it is a solid graphic overall. And taken together the piece overall does a nice job of showing just how restrictive these new pieces of legislation truly are. And how geographically limited in scope they are. Notably, some states people might not associate with seemingly draconian laws are found in surprising places: Pennsylvania, Illinois, Maryland, and New York. But that last point would be best illustrated by another box map.

Credit for the piece goes to K.K. Rebecca Lai.

The 2017–18 Flu Season

Last week I covered the Pennsylvania congressional district map changes quite a bit. Consequently I was not able to share a few good pieces of work. Let’s hope nothing goes terribly wrong this week and maybe we can catch up.

From last Friday we have this nice piece from FiveThirtyEight looking at the spread of influenza this season.

Red is definitely bad
Red is definitely bad

The duller blues and greens give way to a bright red from south to north. Very quickly you can see how from, basically, Christmas on, the flu has been storming across the United States. It looks as if your best bets are to head to either Maine or Montana. Maybe DC, it’s too small to tell, but I kind of doubt that.

As you all know, I am a fan of small multiples and so I love this kind of work. To play Devil’s advocate, however, I wonder if an interactive piece that featured one large map could have worked better? Could the ability to select the week and then the state yield information on how the flu has spread across each state? I am always curious what other other forms and options were under consideration before they chose this path.

Credit for the piece goes to the FiveThirtyEight graphics department.