Lockdowns – The Illusion of Controlling a Virus, part II

Just a brief addendum to part 1:

Below a graph showing a Bayesian Linear Regression, using the OxCGRT index as predictor for deaths per million, for each individual country of the 14 countries I’ve been looking at lately:


What’s interesting to observe here is that the slope of the regression varies  – some countries have a positive slope, others have a negative slope. And as we saw in the previous post, when running a regression on the aggregate data of about 100 different countries, the regression goes completely flat, slope zero…

So, the questions is: does a stricter lockdown result in fewer deaths (negative slope, as is the case for a few of the countries above), or does a stricter lockdown result in more deaths (positive slope, as other countries above)….?

Or is the relationship inverted, that is, does a high death toll result in high level of lockdown, and low death toll in low level lockdown…?

Or can it be the case that the level of lockdown hardly matters at all – as indicated by the aggregate of ~100 countries – that there are other, way more significant factors determining the number of deaths than the level of Lockdown…? 

About swdevperestroika

High tech industry veteran, avid hacker reluctantly transformed to mgmt consultant.
This entry was posted in Bayes, Data Analytics, Epidemics, MCMC and tagged , , , , . Bookmark the permalink.

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