Which Groups Are Still Dying of Covid in the U.S.?

Link: https://www.nytimes.com/interactive/2021/06/10/us/covid-death-patterns.html




“Previously, at the start of the pandemic, we were seeing people who were over the age of 60, who have numerous comorbidities,” said Dr. Krutika Kuppalli, an infectious disease expert at the Medical University of South Carolina. “I’m not seeing that as much anymore.” Instead, she said, hospitalizations have lately been skewing toward “people who are younger, people who have not been vaccinated.”

More than 80 percent of those 65 and older have received at least one dose of a Covid-19 vaccine, compared with about half of those aged 25 to 64 who have received one dose. Data collected by the C.D.C. on so-called breakthrough infections — those that happen to vaccinated people — suggest an exceedingly low rate of death among people who had received a Covid-19 vaccine.

Author(s): Denise Lu

Publication Date: 10 June 2021

Publication Site: New York Times

Who would want to leave New York?

Link: https://blog.datawrapper.de/new-york-city-immigration/



In fact, just having been born here makes me an atypical New Yorker. Of the approximately 8.3 million people who live in the city today, just under half were born in New York State. Eleven percent come from other US states and 40% from the rest of the world. So we’re not wrong to associate New York with immigration—the average New Yorker comes from somewhere else.

I got these numbers from the US Census Bureau, who do their best to estimate not just how many people live in each county, but how they got there: by birth, by migrating from another country, or by migrating from elsewhere in the US. When you take away the people who died, moved abroad, or moved domestically, you’re left with each of these three streams’ net effect on the population that year.[1] Those are the numbers that will show us whether it’s unusual to move away:

Author(s): Rose Mintzer-Sweeney

Publication Date: 3 June 2021

Publication Site: Datawrapper

Four(plus) Ways to Visualize Geographic Time Data

Link: https://policyviz.com/2021/05/11/fourplus-ways-to-visualize-geographic-time-data/



The last visualization I tried was to really embrace the idea of time in the data. Instead of a map or bar chart or something else, I placed the state abbreviations around two clock faces. I know it sounds weird, but take a look at the final version.

I think this is a fun visualization, and it communicates more precisely the exact average starting times than the previous graphs. The two clocks could be combined to one, but I worry it’s not quite as clear, so I tried using the different colors to differentiate the two hours.

Author(s): Jon Schwabish

Publication Date: 11 May 2021

Publication Site: PolicyViz

Rebekah Jones, the COVID Whistleblower Who Wasn’t

Link: https://www.nationalreview.com/2021/05/rebekah-jones-the-covid-whistleblower-who-wasnt/


There is an extremely good reason that nobody in the Florida Department of Health has sided with Jones. It’s the same reason that there has been no devastating New York Times exposé about Florida’s “real” numbers. That reason? There is simply no story here. By all accounts, Rebekah Jones is a talented developer of GIS dashboards. But that’s all she is. She’s not a data scientist. She’s not an epidemiologist. She’s not a doctor. She didn’t “build” the “data system,” as she now claims, nor is she a “data manager.” Her role at the FDOH was to serve as one of the people who export other people’s work—from sets over which she had no control—and to present it nicely on the state’s dashboard. To understand just how far removed Jones really is from the actual data, consider that even now—even as she rakes in cash from the gullible to support her own independent dashboard—she is using precisely the same FDOH data used by everyone else in the world. Yes, you read that right: Jones’s “rebel” dashboard is hooked up directly to the same FDOH that she pretends daily is engaged in a conspiracy. As Jones herself confirmed on Twitter: “I use DOH’s data. If you access the data from both sources, you’ll see that it is identical.” She just displays them differently.

Or, to put it more bluntly, she displays them badly. When you get past all of the nonsense, what Jones is ultimately saying is that the State of Florida—and, by extension, the Centers for Disease Control and Prevention—has not processed its data in the same way that she would if she were in charge. But, frankly, why would it? Again, Jones isn’t an epidemiologist, and her objections, while compelling to the sort of low-information political obsessive she is so good at attracting, betray a considerable ignorance of the material issues. In order to increase the numbers in Florida’s case count, Jones counts positive antibody tests as cases. But that’s unsound, given that (a) those positives include people who have already had COVID-19 or who have had the vaccine, and (b) Jones is unable to avoid double-counting people who have taken both an antibody test and a COVID test that came back positive, because the state correctly refuses to publish the names of the people who have taken those tests. Likewise, Jones claims that Florida is hiding deaths because it does not in­clude nonresidents in its headline numbers. But Florida does report nonresident deaths; it just reports them separately, as every state does, and as the CDC’s guidelines demand. Jones’s most recent claim is that Florida’s “excess death” number is suspicious. But that, too, has been rigorously debunked by pretty much everyone who understands what “excess deaths” means in an epidemiological context—including by the CDC; by Daniel Weinberger, an epidemiologist at the Yale School of Public Health; by Lauren Rossen, a statistician at the CDC’s National Center for Health Statistics; and, most notably, by Jason Salemi, an epidemiologist at the University of South Florida, who, having gone to the trouble of making a video explaining calmly why the talking point was false, was then bullied off Twitter by Jones and her followers.

Author(s): Charles C. W. Cooke

Publication Date: 13 May 2021

Publication Site: National Review

Credit Where Credit is Due: Mary Eleanor Spear

Link: https://medium.com/nightingale/credit-where-credit-is-due-mary-eleanor-spear-6a7a1951b8e6



On page 166 of her 1952 book, in a chapter titled “The Bar Chart”, Spear shows very clearly an early form of a chart type called the Box Plot that she calls the “Range Bar.” …..

What’s interesting about this to me is that if you look up the Wikipedia page for Box Plot, at the present moment, you will not find Spear’s name appearing anywhere in the article. You will, however, read the following:

“Since the mathematician John W. Tukey introduced this type of visual data display in 1969, several variations on the traditional box plot have been described.”

The way I see it, the range bar appearing in Spear’s book is close enough in form to the box plot to warrant a mention on this Wikipedia page. Hopefully, by the time you read this, you’ll be able to find an updated page for the box plot with her name included on it.

Author(s): Ben Jones

Publication Date: 6 August 2019

Publication Site: Nightingale at Medium

Data visualizations: Choosing colors with purpose

Link: https://uxdesign.cc/data-visualizations-choosing-colors-with-purpose-4a672ac0215a



The incorporation of intuitive color maps in data visualizations is an extremely useful tool, but has one major drawback: the information conveyed by the colors is lost to those who cannot distinguish between them. According to the Colour Blind Awareness Organisation (UK-based), approximately 8% of men and 0.5% of women worldwide have some form of color vision deficiency (CVD). Fortunately, certain scientifically derived color maps have been created which are able to maintain distinguishability across various different types of color blindness. Several such color maps are shown below with their corresponding CVD-adjusted perceptions.

Author(s): T. J. Kyner

Publication Date: 30 April 2021

Publication Site: UX Collective

COMIC: How I Cope With Pandemic Numbness

Link: https://www.npr.org/sections/goatsandsoda/2021/04/25/987208356/comic-how-i-cope-with-pandemic-numbness



Each week I check the latest deaths from COVID-19 for NPR. After a while, I didn’t feel any sorrow at the numbers. I just felt numb. I wanted to understand why — and how to overcome that numbness.


Publication Date: 25 April 2021

Publication Site: Goats and Soda at NPR

Datawrapper Dataviz Book Club

Link: https://notes.datawrapper.de/p/bookclub-tufte


1. What was the most surprising thing you’ve learned? Choose a text passage, and explain how it challenged something you assumed. (Type up the text passage / phrase, and tell us on which page we can find it!) 

2. Select one of your favorite data visualizations. Is it working well becauseof a principle that Tufte explained? Or do you appreciate something about it although it goes against Tufte’s principles? – give us a link to the data vis! If you need to upload something, but it on https://imgur.com/ or Twitter. 

3. Having read the book, what will you do differently the next time you design a chart?

Date Published: August 2018

Date Accessed: 21 April 2021

Publication Site:

Mortality with Meep: Location of Death Data for 1999-2020, Entire United States

Link: https://marypatcampbell.substack.com/p/mortality-with-meep-looking-at-location



For a few locations, it’s pretty clear that COVID explains almost all their excess deaths: inpatient healthcare facilities and nursing homes. Indeed, it looks like over 100% of the nursing home excess mortality came from COVID, which accords with what I see with excess mortality for older people.

However, there is a lot of excess mortality for people who died at home, and most of that is currently unexplained by COVID.

I don’t think it will be — I think we will find those excess diabetes, heart attack, and ‘unintentional injury’ deaths will have been at home, and because of lockdowns there weren’t other people around to get these people to treatment before they died. This accords with what Emma Woodhouse saw for Illinois – that pattern holds for the entire U.S., it seems.

Author(s): Mary Pat Campbell

Publication Date: 20 April 2021

Publication Site: STUMP at substack

How much is your community getting under the American Rescue Plan? A searchable database for the nation is here – Wirepoints



Embedded below are a set of searchable databases that provide the estimated allocation of the $360 billion in direct government aid to states, counties and cities under the $1.9 trillion American Rescue Plan. The remaining stimulus includes funding for schools and other programs, for which detailed data is not yet available.

The $360 billion is split as follows: State governments are set to receive $230 billion in direct and capital project grants, county governments will receive $65 billion, and municipal governments will receive the other $65 billion.

Author(s): Ted Dabrowski, Mark Glennon, John Klingner

Publication Date: 17 April 2021

Publication Site: Wirepoints