Case Severity Index vs. staff absence
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Essential background reading for this slide is COVID uncovered (part 6) and COVID uncovered (part 7). What I’ve done here is the equivalent of pulled pork. The two grey lines represent the overall mean values for each series and are placed to divide the scatterplot into equivalent quadrants.
If staff absence due to COVID is a decent proxy for disease prevalence and the ratio of MV to general bed use for COVID cases is a reasonable proxy for case severity (CSI) then the top right and bottom left quadrants make sense. Going against the grain are the top left and bottom right quadrants; the ‘grain’ being my claim that these are useful proxy measures of what I believe them to be.
There are some mighty curious features here, starting with the incredibly linear relationships for 2020/Q2, 2021/Q1 and 2022/Q1. When I see highly linear forms lurking in a cloud like this I begin to doubt the validity of the data; that is, it feels a trifle ‘massaged’… or policy driven?
The relationship reversal between 2020/Q2 and 2021/Q1 is rather interesting, as is the lowest MV ratio series for 2022/Q1 (Omicron fair and mild). Given that early exposure for frontline staff will have been pretty darn certain then I look upon the sway of points from right to left as indicative of increasing immune function. In the same manner I look upon the flow of points from top to bottom as indicative of lessening viral bite. That 2022/Q1 should stand out thus ties in nicely with reports of Omicron being a very different but rather mild beast.
The fly in the ointment for my graceful hypothesis is that strange series for 2021/Q1 that looks bolted-on. It is interesting that this period should correspond with the first big push in vaccine rollout. Then again, we have the matter of false positive and false negative test results to consider, along with all manner of policy change. There is much to ponder on methinks, so I shall go get the…
Kettle on!