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 http://www.linkedin.com/in/deepdevelopment/ and https://667-per-cm.net/about
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