Lockdown madness goes on, even as science shows COVID isn’t that deadly

By Graham
There is a growing body of evidence which suggests that the coronavirus related disease COVID-19, is not nearly as deadly as is being widely reported in the media and as is believed by the public.
Here is one of many serologic studies which uses the best scientific evidence available to demonstrate this fact:
“Fear of Covid-19 is based on its high estimated case fatality rate — 2% to 4% of people with confirmed Covid-19 have died, according to the World Health Organization and others,” he wrote in the Wall Street Journal. “So if 100 million Americans ultimately get the disease, two million to four million could die. We believe that estimate is deeply flawed. The true fatality rate is the portion of those infected who die, not the deaths from identified positive cases.”
This study was performed by researchers are Stanford University, and follows a similar study, which found similar results, performed by Bonn University. It also follows the data gathered early on from the cruise ships, which provided near-laboratory like conditions for seeing the spread of the virus on a population crammed together under contagion-inducing conditions. Please understand that this is not right-wing propoganda, these are not fringe institutions. They are among the best scientists in the field, and the message is clear:
Coronavirus SARS-Cov-2, and the COVID-19 disease it causes, kill only about 0.5% of its victims. The clear majority (over 80%) of the deaths are among the very old, and the vast majority among those who are over 65 and have preexisting conditions (93%). While of course their passing is tragic, a disease that kills someone who is very close to their natural life expectancy is very different to one that cuts down healthy people in their prime.
What is more, the Lockdowns that have been put in place have no proven impact on the spread of this disease.
What the lockdowns certainly do cause is a massive contraction in economic activity. On this point, there seems to be some confusion on what such a contraction means. This isn’t a case of ‘the banks losing money’, rather it means there are fewer goods and services in the economy – an estimated reduction in 10% for the current year in most countries. Concretely, this will certainly mean an increase in poverty by at least 10% – fewer resources to fund schools and hospitals. Many, many more people will die from this that could possibly be the case from the virus itself.
In addition, the lockdowns represent an unprecedented curtailment of our civil liberties. Governments everywhere have overstepped their constitutional limits, locking their citizens in their houses and literally surpressing public debate. The only public forums available for discussion are online businesses, who have a direct economic incentive to keep the non-virtual economy closed for as long as possible. Not surprising therefore, that those who oppose lockdowns are being shadow-banned or blocked on Facebook and Twitter (e.g. Brazil’s democratically elected president).
Those of you who know me know that I am a measured, reasonable person. I have been educated in economics at a leading university and have worked number-crunching for public institutions for many, many years. In that context, I write policy documents that by their nature avoid hyperbole and sensationalist conclusions. But before I go, I just want to leave you with the following graph, to help you understand that not everyone is losing out, and that the very companies that are winning in this crisis are the ones who have the most control over how we understand this crisis:
covid amazon stock price
 Category: Economics Politics

Leave a Reply

Comments Protected by WP-SpamShield Anti-Spam

As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Adjusted R square: 75.191

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP:

fitted-death-rates-against-actual-values

While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.

×

We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

Adjusted R square: 0.980438073

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values:

fitted-emissions-to-gdp-against-actual-values

×