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.)