Nothing you are going to read is going to be a big shock to you if you’ve been paying any attention at all over the past two months. We’ve seen outrageous restrictions imposed upon personal mobility and activity and businesses shuttered, many of them forever, based on an ill-thought-out scheme of remedies that may work in an experimental setting but have never been tested or validated on a large scale.
The problems are threefold. First, we don’t understand the pathogen. Since it first emerged, we have had the initial assurances from our experts that it was no big deal morph into it being some kind of Black Plague on steroids, to it is bad but not terrible. Second, driven by the modelers and the experts, political leaders have been stampeded into taking the most draconian measures imaginable under the circumstances because they don’t want to be blamed for a single death and they lack the guts to call bullsh** on what are obviously bullsh** projections. Third, because there is no logical off-ramp to the hysteria, the modelers, the experts, and the politicians have to dig in and extend the illegal measures they have adopted as long as possible in the hope that the virus recedes naturally as the heat and humidity of summer approach.
Now, as we get more information, it becomes increasingly obvious that virtually nothing we have done to ourselves matters. The author of the post is Dr. Wilfred Reilly of Kentucky State University. His academic specialty is subjecting political claims to empirical testing.
The question the model set out to ask was whether lockdown states experience fewer Covid-19 cases and deaths than social-distancing states, adjusted for all of the above variables. The answer? No. The impact of state-response strategy on both my cases and deaths measures was utterly insignificant. The ‘p-value’ for the variable representing strategy was 0.94 when it was regressed against the deaths metric, which means there is a 94 per cent chance that any relationship between the different measures and Covid-19 deaths was the result of pure random chance.
The only variable to be statistically significant in terms of cases and deaths was population (p=0.006 and 0.021 respectively). Across the US states, each increase in the population of 100,000 correlated with 1,779 additional Covid-19 cases, even with multiple other factors adjusted for. Large, densely populated areas are more likely to struggle with Covid-19, no matter what response strategy they adopt – although erring on the side of caution might make sense for global megacities such as New York and Chicago.
(You can request the dataset for the regression model here, something the clown from Imperial College that set off this nonsense will not do.)
If you have followed my posts on the subject, this is not an unusual finding because fatalities, from all causes, in the United States were already below expectations even before the imposition of ‘social distancing’ measures.