Stanford scientists: Data may not support widespread coronavirus lockdowns

Two scientists from Stanford University have claimed that new data calculations show that COVID-19 could be far less deadly than projections based on confirmed cases suggest and that the large-scale shutdowns that have brought the economy to a virtual halt may be unnecessary, the Washington Examiner reports.

Drs. Eran Bendavid and Jay Bhattacharya published an article in the Wall Street Journal titled, “Is the Coronavirus as Deadly as They Say?” that raises questions about the true number of coronavirus cases and the attendant death rates health officials are assuming will result from the virus as they recommend widespread shutdowns and other protective measures.

“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,” the article noted.

Such a fatality rate would mean that between 2 and 4 million Americans would die from the disease if 100 million were to contract it, but data collected from areas characterized by more widespread testing tell a much different story, the professors say.

Higher-than-reported infection rate

According to the piece, when a planeload of passengers from Wuhan, China were tested over a period of 14 days after landing in the U.S., 0.9% of them were positive for coronavirus. When those numbers are extrapolated to the 20 million people living in that region, there theoretically should have been around 178,000 cases there at the time, but only about 6,000 were reported.

The professors say this would have put the death rate in Wuhan at roughly 0.3%. When they looked at other areas of nearly universal testing like the northern Italian town of Vò and the roster of the NBA, they arrived at projected actual death rates of .016% in Italy and even lower in the case of the NBA player population.

These numbers are even lower than death rates for the flu — a lot lower in some cases. “Epidemiological modelers haven’t adequately adapted their estimates to account for these factors,” the researchers argue in the Wall Street Journal article.

These findings beg the question of why, if the death rate of COVID-19 in the United States is really only a fraction of the death rate for the flu, millions of people in dozens of states are in a lockdown the likes of which have never seen before?

Incomplete testing data to blame

In the U.S., testing has been so incredibly slow that many, if not most, infected people have never even been tested for the virus. Even now, many are being refused tests if they are exhibiting only mild symptoms of the illness, which means that the numbers of confirmed cases are likely painting an inaccurate picture of what is truly going on.

The professors theorize that there were likely many undiagnosed cases of the virus in the U.S. even before January because, as they point out in their article, tens of thousands of people flew into the U.S. from Wuhan in December of 2019, long before President Donald Trump closed the border to Chinese arrivals.

The only true way to know the actual extent of infection in the U.S., the professors say, is to do comprehensive antibody testing, which would show who has already had the coronavirus and has since recovered from it. These tests will be available in the relatively near future, but it is too soon to tell how widespread this kind of analysis will be.

But the key takeaway, according to the scientists, is that “a universal quarantine may not be worth the costs it imposes on the economy, community and individual mental and physical health. We should undertake immediate steps to evaluate the empirical basis of the current lockdowns,” they wrote. If there is no data to support putting millions of people out of work by imposing widespread restrictions on commerce and movement, they argue, we definitely shouldn’t.

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