For the first time in American and perhaps world history, we are witnessing something unique: the intentional suppression by governments of a very large percentage of economic activity to fight a disease. There are estimates that the US economy will experience a 38% decline from its recent peaks. Already, millions are unemployed and thousands of businesses are closed, many of which we are unsure they will ever reopen. The stock market has experienced trillions in lost value affecting the pension plans of millions.
The proffered reason is that this was a true “crisis” that necessitated such action. To justify the response to the crisis, there had to be evidence that the economic suppression would quickly end the crisis. The problem is how does one distinguish a crisis that warrants such a drastic response versus something that is part of normal human existence? As a corollary, how do we know the drastic response will improve the situation?
Fact: Sadly, 2.8 million Americans die every year. That is 240,000 every month and 8,000 every day without factoring in the Wuhan virus. Guess what? Americans, being human, are mortal. Likewise, every year, Americans are warned against the impending flu season. This does not mean that the Wuhan virus is equivalent to the flu, but there has to be some benchmark for comparative reasons against which one can say there is a “crisis.” According to the CDC, over 143,000 Americans have died of the seasonal flu over the period 2016-2019. It accounts for 1-2.5% of all deaths in a year which is significant, but never shut down an economy.
What makes the Wuhan virus different? There has to be proof to justify the reaction and for that, we need to go back to the beginning of the “crisis.” What got the ball rolling was a modeled projection that scared the living crap out of policy-makers. The kick-off was the study by Neal Ferguson stating that without drastic action, the Wuhan virus would kill up to 2.2 million Americans. Such modeled projections are the equivalent of a scientific hypothesis that only becomes usable by policy makers through the accumulation of data. Ferguson simply had no, or faulty, data at the time.
With the accumulation of data, Ferguson and his model gurus quickly backed off their projections and the government turned to the IHME models at the University of Washington projecting up to 200,000 dead Americans due to Wuhan, or a 90% reduction from the Ferguson model. A quick series of reductions followed that by the time April 8th rolled around, the number was in the 60,000 range- or about how many people died from the flu in 2017-2018. The projections also had dire warnings about hospital bed availability which as of now falls somewhere less than “dire.”
But what about deaths? On April 14th, the US was averaging about 2,000 per day rather consistently for several days. But on the 14th, the number of deaths suddenly shot up to 6,000 (according to Worldometers). Surely, the decision by New York City to add 3,778 new deaths where the deceased had no positive test for the virus had anything to do with that, right? There is also the problem of co-morbidity- people who would have died regardless of Wuhan virus. The incentives for reporting deaths is obvious: mainly federal money and Democrats lusting for power. The big problem is how much they are inflated.
It is impossible to answer these questions since only 3.2 million people have been tested for the virus as of April 16th, or 1% of the population. Without the data, it is quite possible that the death rate is 10% of what is officially reported. We do not know what percentage of the population has been exposed to it, yet showed no symptoms or mild symptoms. One way to find out is by comparing deaths this year against last year for comparable time periods and attributing the “excess deaths” to the Wuhan virus. Even still, you would need random testing of the general population to get a true handle on the mortality rates, the degree of infection, and how contagious the disease is to make sound policy decisions.
We then come to the line that the forced lockdowns, social distancing and economic suppression are what created the declines in hospitalizations and deaths. These were instituted in many states starting in mid-March and by the beginning of April, the rate of deaths and hospitalizations had declined. Eureka! It worked! However, all these learned people making these projections have surely heard of the null hypothesis which in this case asserts that any jurisdiction would fare just as well in terms of prevalence or death without the economic suppression. To exclude the null hypothesis, you have to compare the lenient jurisdictions against the more draconian ones.
Fortunately, we have such a natural laboratory here in the United States. South Dakota has the least draconian measures in the country for which Governor Kristi Noem has been roundly criticized in the liberal press. Contrary to the reports, Noem has encouraged social distancing and hygiene, but has imposed no mandatory restrictions on businesses and, in a truly uniquely American approach, has left it to individuals to make decisions for themselves. As of this writing, South Dakota has 1,311 reported cases and 7 deaths. Compare that to New York with its 223,700 cases and almost 15,000 deaths. Yes- South Dakota has a smaller population and is less dense than New York, but consider that New York has 22 times the population of South Dakota, but 2,000 times the number of deaths.
The reason put forth by the modelers in that South Dakota has not reached their peak yet, or that they are at an earlier stage in the disease. That flies in the face of facts- both states seem to have hit a plateau simultaneously. The Leftist press is signaling that the Wuhan virus is coming to rural America, but that does not seem to be the case. Unsurprisingly, the IMHE projections for South Dakota have been revised seriously downward since the original projections were ridiculously high. It’s amazing what a little bit of data does to models.
So what really is going on here? The answer may be found with Isaac Ben-Israel, head of Securities Studies at Tel Aviv University and Chairman of Israel’s National Council for Research and Development. In pertinent part, the professor wrote:
…simple statistical analysis demonstrates that the spread of COVID-19 peaks after about 40 days and declines to almost zero after 70 days — no matter where it strikes, and no matter what measures governments impose to try to thwart it.
This was published on a website called Homeland Security News Wire. Having never heard of the website, this writer checked on it with a website called Mediabiasfactcheck and found that they rated the site “least biased” with a “high” degree of factual reporting. Most importantly, Dr. Ben-Israel’s finding are based on actual data and are not a theoretical model.
In a new world where accurate data is needed, public policy has been placed in the grips of doctors and alleged experts who believe they understand what is going on because of what they think are sophisticated models. We’ve seen it before with climate change. And sadly, not a single one of them will lose their well-paid jobs in government or academia.
As for the Democrats, this is a their dream become reality. They are basically implementing the Green New Deal through the fight against a virus. Hopefully, enough Americans have now witnessed and lived through what models, “experts,” technocrats and Democrats have wrought to reject their “visions.” When the US death toll tops out somewhere in the vicinity of that of the seasonal flu- maybe 60,000-70,000- will anyone remember?