Political and demographic associations with changes in COVID-19 rates at the tails

A few days ago, I wrote a post about poltical and demographic associations with changes in COVID-19 rates over all U.S. counties. Today, I’m augmenting that. For here, rather than considering all counties, I limited the study to counties with demonstrably large increases or decreases in COVID-19 prevalence.

Rather than working with 3083 counties, this works with 742 of them. Essentially the cutoff was that the response calculated had to exceed 7.02 in magnitude:

Two regressions were repeated. The first was a standard linear regression of response versus all the remaining predictors. The improvement in R^{2} compared with the linear regression done with the complete dataset was remarkable: Adjusted R^{2} was 0.47 rather than 0.16. The second was the random forest regression. Here, too, the improvement in R^{2} was substantial: 0.71 versus 0.54 previously.

Here’s the result of the linear regression:

And, while the number of variables selected in the importance screening did not change, the ones having a consistent (monotonic) effect did. Those contributing to an increase in COVID-19 became:

  • otherpres16
  • otherhouse16
  • hispanic_pct
  • PerCapitaDollars
  • PctChgFrom2017

Those contributing to a decrease in COVID-19 became:

  • repsen16
  • rephouse16
  • black_pct
  • clf_unemploy_pct
  • lesshs_pct
  • lesshs_whites_pct

Note, for contrast, the random forests regression based upon all the counties had those variables contributing to an increase being:

  • otherpres16
  • otherhouse16
  • hispanic_pct
  • PerCapitaDollars
  • PctChgFrom2017

The random forests regression based upon all the counties had these variables:

  • demhouse16
  • black_pct
  • clf_unemploy_pct
  • lesshs_pct
  • lesshs_whites_pct
  • trump.obama.ratio

contributing to a decrease in COVID-19 prevalence.

The current choices make more sense, in addition to being more statistically notable because of the increased R^{2}. But increase in Republican Senate and House support associated with decrease in COVID-19? Hmmmm.

About ecoquant

See https://667-per-cm.net/about. Retired data scientist and statistician. Now working projects in quantitative ecology and, specifically, phenology of Bryophyta and technical methods for their study.
This entry was posted in Five Thirty Eight, Tamino. Bookmark the permalink.

Leave a reply. Commenting standards are described in the About section linked from banner.

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.