A new way to visualize the surge in Covid-19 cases in the U.S.



The number represented by the line could be thought of as the velocity of cases in the U.S. It tells us how fast case counts are increasing or decreasing and does a good job of showing us the magnitude of each wave of cases.

The chart, however, fails to show the rate of acceleration of cases. This is the rate at which the number of new cases is speeding up or slowing down.

As an analogy, a car’s velocity tells you how fast the car is going. Its acceleration tells you how quickly that car is speeding up.

Using Covid-19 case data compiled by the Center for Systems Science and Engineering at Johns Hopkins University and Our World in Data, combined with data from the Centers for Disease Control and Prevention, STAT was able to calculate the rate of weekly case acceleration, pictured below.

Author(s): Emory Parker

Publication Date: 26 July 2021

Publication Site: STAT News

Skip the fireworks this record-dry 4th of July, over 150 wildfire scientists urge the US West

Link: https://theconversation.com/skip-the-fireworks-this-record-dry-4th-of-july-over-150-wildfire-scientists-urge-the-us-west-163561



For decades, one of the most striking and predictable patterns of human behavior in the western U.S. has been people accidentally starting fires on the Fourth of July. From 1992 to 2015, more than 7,000 wildfires started in the U.S. on July 4 – the most wildfires ignited on any day during the year. And most of these are near homes.

Author(s): Philip Higuera, Alexander L. Metcalf, Dave McWethy, Jennifer Balch

Publication Date: 1 July 2021

Publication Site: The Conversation

Delta is fast becoming the world’s dominant strain of SARS-CoV-2

Link: https://www.economist.com/graphic-detail/2021/06/29/delta-is-fast-becoming-the-worlds-dominant-strain-of-sars-cov-2



AT A PRESS conference at the White House on June 22nd Anthony Fauci, the director of America’s National Institute of Allergy and Infectious Diseases, issued a warning. The delta variant of the SARS-CoV-2 virus, first identified in India in February, was spreading in America—and quickly. “The delta variant is currently the greatest threat in the US to our attempt to eliminate covid-19,” declared Dr Fauci. Boris Johnson, Britain’s prime minister, issued a similar warning a week earlier. To contain the rapid spread of the variant, European countries and Hong Kong have tightened controls on travellers from Britain.

According to GISAID, a data-sharing initiative for corona- and influenza-virus sequences, the delta variant has been identified in 78 countries (see chart). The mutation is thought to be perhaps two or three times more transmissible than the original virus first spotted in Wuhan in China in 2019. It is rapidly gaining dominance over others. According to GISAID’s latest four-week average, it represents more than 85% of sequenced viruses in Bangladesh, Britain, India, Indonesia and Russia. It may soon be the most prevalent strain in America, France, Germany, Italy, Mexico, South Africa, Spain and Sweden. (GISAID does not, in its summary data, distinguish between delta, B.1.617.2, and the “delta plus” mutation, AY.1, AY.2.)

Publication Date: 29 June 2021

Publication Site: The Economist

Data visualisation using R, for researchers who don’t use R

Link: https://psyarxiv.com/4huvw



In addition to benefiting reproducibility and transparency, one of the advantages of using R is that researchers have a much larger range of fully customisable data visualisations options than are typically available in point and-click software, due to the open-source nature of R. These visualisation options not only look attractive, but can increase transparency about the distribution of the underlying data rather than relying on commonly used visualisations of aggregations such as bar charts of means. In this tutorial, we provide a practical introduction to data visualisation using R, specifically aimed at researchers who have little to no prior experience of using R. First we detail the rationale for using R for data visualisation and introduce the “grammar of graphics” that underlies data visualisation using the ggplot package. The tutorial then walks the reader through how to replicate plots that are commonly available in point-and-click software such as histograms and boxplots, as well as showing how the code for these “basic” plots can be easily extended to less commonly available options such as violin-boxplots. The dataset and code used in this tutorial as well as an interactive version with activity solutions, additional resources and advanced plotting options is available at https://osf.io/bj83f/. This is a pre-submission manuscript and tutorial and has not yet undergone peer review. We welcome user feedback which you can provide using this form: https://forms.office.com/r/ba1UvyykYR. Please note that this tutorial is likely to undergo changes before it is accepted for publication and we would encourage you to check for updates before citing.

Nordmann, Emily, Phil McAleer, Wilhelmiina Toivo, Helena Paterson, and Lisa M. DeBruine. 2021. “Data Visualisation Using R, for Researchers Who Don’t Use R.” PsyArXiv. June 21. doi:10.31234/osf.io/4huvw.

Additional materials: https://osf.io/bj83f/wiki/home/

Author(s): Emily Nordmann, Phil McAleer, Wilhelmiina Toivo, Helena Paterson, Lisa M. DeBruine

Publication Date: 21 June 2021

Publication Site: PsyArXiv

Mortality Nuggets: NYT Misleads, COVID Deaths Down, and Car Crash Fatalities Up

Link: https://marypatcampbell.substack.com/p/mortality-nuggets-nyt-misleads-covid



I want you to notice something — the blue bars are the “with COVID” portion of deaths, and the chartreuse bars are the ones “without COVID”. The bars are weekly counts of deaths when they occurred. Ignore the most recent weeks because they don’t have full data reported yet.

The red pluses indicate excess mortality, defined as exceeding the 95th percentile for expected mortality for that week (so it includes seaonality). You can see the excess mortality from the 2017-2018 flu season, which was bad for a flu season.

The non-COVID mortality has been in excessive mortality range for almost all 2020 after March. But since the beginning of 2021, it has dropped off…. and COVID mortality has also dropped off.

I think we may be almost in “normal” range soon. We shall see!

Author(s): Mary Pat Campbell

Publication Date: 13 June 2021

Publication Site: STUMP at substack

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