Examining Colorado’s Covid-19 Outbreak Using Control Charts

What helps us understand what’s going on, in a useful, actionable way?

Story update 5/6/2020: The charts and some data have been updated to reflect the data available on the date this article was published.

During the Covid-19 stay-at-home order in Colorado, I've become increasingly frustrated by Covid-19 charts. Most of what I see are cumulative column charts, which don't give any real insight into what's going on. Are we really flattening the curve?

So I decided to use the state's Covid-19 statistics for Colorado and Denver county, and see what I could learn using control charts. Control charts have been around for almost 100 years. They use formulas to calculate control limits that encompass 99.7 percent of the data points. This makes it easy to monitor any process and detect process shifts and "out of control" conditions.

Source: https://covid19.colorado.gov/case-data Click image for larger view.

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Comments

XmR charts are better than the alternatives

I've also been creating and using XmR ("Process Behavior Charts") with Covid cases and deaths in certain locations that matter to me.

I see Dr. Wheeler's point, but I'd also suggest that XmR Charts are far more helpful than any alternatives, such as two-data-point comparisons or other limited views of data.

The XmR charts I've made show periods of predictability... then there are shifts. The system has changed. But why? I agree that's what really matters.