A step toward understanding (part 3)
Using factor analysis to get a handle on the pandemic
In A step toward understanding (part 1) I kicked off by boiling down 38 pandemically-flavoured independent variables into a mélange of just 5 orthogonal factors I called nationwide testing, COVID cases + clinical tests, COVID deaths, prevalence + CFR, and in-hospital test rate. These five beauties explained 93.4% of the variation available in the 38-dimension dataset and glistened like edible jewels in the sun.
In A step toward understanding (part 2) I used the same technique to boil down 27 NHS England hospital activity variables into a set of just 3 orthogonal factors I called COVID workload, non-COVID workload and Case balance. These three gems explained 93.5% of the variation available in the 27-dimension dataset.
Pearson Correlation
This morning I am going to bring these 8 factors together and see how they mesh, and I shall start with something as plain and simple as a Pearson bivariate correlation matrix with a dash of colour1:
First row…
Now this is rather interesting. We observe a…