COVID-19 Data Dispatch

Category: Miscellaneous

  • Entrepreneurial training for the COVID-19 Data Dispatch

    This newsletter is about to step up its game in a big way. I am absolutely honored and thrilled to announce that I’ve been selected for the inaugural cohort of the Entrepreneurial Journalism Creators Program.

    This program is a new 100-day course from the Craig Newmark Graduate School of Journalism at the City University of New York. Starting this coming week, I will learn how to better understand the needs of my audience (that’s you!) and develop a sustainable journalism project. The other 19 students in my cohort include journalists from around the world, working on projects ranging from local news to global health reporting. You can read more about the program and its participants here.

    I’m incredibly grateful to all of the readers who have supported this project so far, whether you subscribed to my first issue or just found this newsletter last week. Thank you for your support and feedback on how I can best make COVID-19 data accessible. I’m excited to share what I learn in my course with you, and to grow the scope and resources offered by this project.

  • How to understand COVID-19 numbers

    OG readers may remember that, in my first issue, I praised a ProPublica article by Caroline Chen and Ash Ngu which explains how to navigate and interpret COVID-19 data. I was inspired by that article to write a similar piece for Stacker: “How to understand COVID-19 case counts, positivity rates, and other numbers.”

    I drew on my experience managing Stacker’s COVID-19 coverage and volunteering for the COVID Tracking Project to explain common COVID-19 metrics, principles, and data sources. The story starts off pretty simple (differentiating between confirmed and probable COVID-19 cases), then delves into the complexities of reporting on testing, outcomes, and more. As a reader of this newsletter, you likely already know much of the information in the story, but it may be a good article to forward to friends and family members who don’t follow COVID-19 data quite so closely.

    (I also made a custom graphic for the “seven-day average cases” slide, which was a fun test of my burgeoning Tableau skills.)

  • My insights on COVID-19 data reporting

    I recently had the honor of speaking to Bara Vaida, from the Association of Health Care Journalists (AHCJ), about my work at Stacker, the COVID Tracking Project, and this newsletter. The full interview is up on AHCJ’s site, but I wanted to highlight my answer to Bara’s question, “What would you say are the common mistakes that you see in how COVID-19 data is reported?”:

    I think it is not contextualizing data appropriately. You have to explain what the data mean. For example, you can say a state’s positivity rate fell from one week to the next, but it is important to explain the numerator and the denominator ― the number of tests that were completed and how many of those tests were positive. And you have to explain that positivity rate in the context of what is happening in the state. Is the state actually doing more testing, or did it have to shut down testing centers because of a hurricane, causing both the number of tests and the number of positives to go down — this happened in Florida a few weeks ago. And also, don’t forget there are real people behind these numbers. It’s always important to remember that.

    I also spoke to education reporter Alexander Russo for his recent column in Phi Delta Kappan. The article provides advice geared towards journalists covering COVID outbreaks in schools, but it’s also a useful primer for teachers, parents, and anyone else closely following school data.