COVID-19 Fatality Rates May Be Vastly Overblown, Standford Medical Professors Bendavid and Bhattacharya Claim COVID-19 Fatality Rates May Be Vastly Overblown, Standford Medical Professors Bendavid and Bhattacharya Claim

COVID-19 Fatality Rates May Be Vastly Overblown, Standford Medical Professors Bendavid and Bhattacharya Claim

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We are in the midst of a historic economic shut down.

Better safe than sorry, as they say.

But are the fundamental numbers of the COVID-19 pandemic vastly overblown?

Was Trump onto something when he claimed that the media panic around the novel coronavirus was a "hoax."

Potentially, if the researchers at Standford Health Policy have anything to say about it.

Current models suggest that the COVID-19 fatality rate may be too high by "orders of magnitude."

Translation: a universal lockdown (like we're currently experinecing) "may not be worth the costs."

Right now, the current estimates say that COVID-19 is 10 times more deadly than the flu. 

But if the Standford study is accurate, then the flu may actually be 10 times more deadly than COVID-19.

More details on the new study below:

Professors Eran Bendavid and Jay Bhattacharya of the Standford Health Policy have released new models showing that the current fatility rates being publicized may be "too high by orders of magnitude."

If this is true, will the media frenzy and panic around the COVID-19 pandemic calm down?

Will the U.S. economy reopen sooner rather than later?

There are still many variables that need to be seen and confirmed, but the latest model shows a lot of hope.

Bendavid and Bhattacharya published their findings in the Wall Street Journal:

If it’s true that the novel coronavirus would kill millions without shelter-in-place orders and quarantines, then the extraordinary measures being carried out in cities and states around the country are surely justified. But there’s little evidence to confirm that premise—and projections of the death toll could plausibly be orders of magnitude too high.

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. 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.

The latter rate is misleading because of selection bias in testing. The degree of bias is uncertain because available data are limited. But it could make the difference between an epidemic that kills 20,000 and one that kills two million. If the number of actual infections is much larger than the number of cases—orders of magnitude larger—then the true fatality rate is much lower as well. That’s not only plausible but likely based on what we know so far.


The best (albeit very weak) evidence in the U.S. comes from the National Basketball Association. Between March 11 and 19, a substantial number of NBA players and teams received testing. By March 19, 10 out of 450 rostered players were positive. Since not everyone was tested, that represents a lower bound on the prevalence of 2.2%. The NBA isn’t a representative population, and contact among players might have facilitated transmission. But if we extend that lower-bound assumption to cities with NBA teams (population 45 million), we get at least 990,000 infections in the U.S. The number of cases reported on March 19 in the U.S. was 13,677, more than 72-fold lower. These numbers imply a fatality rate from Covid-19 orders of magnitude smaller than it appears.

How can we reconcile these estimates with the epidemiological models? First, the test used to identify cases doesn’t catch people who were infected and recovered. Second, testing rates were woefully low for a long time and typically reserved for the severely ill. Together, these facts imply that the confirmed cases are likely orders of magnitude less than the true number of infections. Epidemiological modelers haven’t adequately adapted their estimates to account for these factors.

In their report, Bendavid and Bhattacharya both emphasize that this does not mean that the COVID-19 pandemic should not be taken seriously.

It is clearly a deadly virus with the potential to cause a lot of harm.

But if their model is correct, then the fatility rate may actually be a tenth of the flu mortality rate... not 10 times the flu mortality rate!

During the entire COVID-19 panic, Trump has been a voice of calm and reason.

He has acted quickly and decisively, even as Democrat leaders rushed to politically attack him.

The Blaze has more details on the costs of the lockdown and how these new findings might potentially shape our response:

Bendavid and Bhattacharya say that if they are right about the lower lethality of the epidemic, public policy experts should focus their measures on protecting the elderly and expanding medical capacity.

"Hospital resources will need to be reallocated to care for the critically ill patients. Triage will need to improve. And policy makers will need to focus on reducing risks for older adults and people with underlying medical conditions."

The pair conclude that if their estimates are right, then the universal quarantine measures "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 added.

The Trump administration has been extremely transparent regarding its actions concerning COVID-19.

It's important to remember, however, that our leaders will always have access to important (i.e. classified) information before the general public.

As President Trump continues to speak in hopeful terms concerning the virus, we can only wonder: is there a factual basis for his optimism?

This study suggests, "Yes!"

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