In
the early days of the COVID-19 panic—about three weeks ago—it was common to
hear both of these phrases often repeated:
- “The
fatality rate of this virus is very high!”
- “There
are far more cases of this out there than we know about!”
The
strategy of insisting that both these statements are true at the same time has
been used by politicians to implement “lockdowns” that have forced business to
close and millions to lose their jobs. For instance, on March 12, Ohio
Department of Health director Amy Acton insisted that “over
100,000” people are “carrying this virus in Ohio today.” The state began to
implement “stay-at-home” lockdown orders that day.
At
the time, the World Health Organization (WHO), the media, and others were reporting that 2 to 4 percent of
people with COVID-19 would die. Taking the low-end 2 percent number, and
allowing for an incubation period, this would mean that two weeks after Acton’s
announcement—assuming that the lockdown was 100 percent effective and not
a single additional person caught the disease—two thousand Ohioans would likely
be dead of COVID-19. But as of April 17, more than a month later, and after a month of the disease
spreading through grocery stores and other “essential” areas of commerce, about
418 Ohioans have died of COVID-19.
Clearly,
something doesn’t add up.
At
the time, Acton was lampooned by some for presumably inflating the number of
infections in the state. Indeed, the very next day she backpedaled, saying she was only guessing.
As
more research comes in, however, it may be that Acton wasn’t wildly inaccurate
in her “guesstimate” after all. Medical researchers and epidemiologists are
increasingly claiming that the COVID-19 virus has spread much more quickly and
is much more prevalent than has long been assumed. And if that is true, then
the percentage of people COVID-19 cases that result in death are far lower than
is assumed. If so, Acton was still wrong, but she was more wrong in her
assumptions about fatality rates than about total cases.
Here’s
why:
When
people say “death rate” or “fatality rate” they generally mean “case fatality
rate” (CFR). This is simply the number of people who die from a disease divided
by the number of cases. If there are 10,000 cases and 100 people die from
the disease, the CFR is 1 percent. (This is not to be confused with “mortality
rate,” which is the number of deaths divided by the entire population.)
To
calculate the CFR accurately we have to know what the total number of cases
is and also know how many people have died from the disease. If the total
number of cases is bigger than we think, then the fatality rate is smaller than
we think.
How
Death Rates Are Affected by Government Data Collection Methods
Counting
the number of deaths has been far easier than counting total cases. Due to
“severity bias,” people who have presented severe symptoms or have
died have been far more likely to be tested for COVID-19 than have people
with few symptoms who never required medical attention. As one epidemiologist
quoted by the New York Times noted:
“To know the fatality rate
you need to know how many people are infected and how many people died from the
disease,” said Ali H. Mokdad, a professor of health metrics sciences at the
Institute for Health Metrics and Evaluation. “We know how many people are
dying, but we don’t know how many people are infected.”
Some
deaths of course are missed, especially among those who die at home. But as
the Times article concludes:
the missing data on deaths in
the deaths-to-infections ratio is still almost certain to be dwarfed by the
expected increase in the denominator when the total number of infections is
better understood, epidemiologists say. The statistic typically cited by
mayors and governors at Covid-19 news conferences relies on a data set that
includes mostly people whose symptoms were severe enough to be tested.
Put
another way, the case totals often cited by politicians are nothing more than
wild guesses.
Indeed,
many researchers and other observers have claimed that total cases numbers were
considerably higher than was known from testing.
“Vermont could have 16 times more
infections than officially reported,” one March 18 headline reads.
But this estimate doesn’t apply just to Vermont. The headline comes from a
nationwide estimate of cases from Stanford epidemiologist Steve Goodman:
Goodman says the 16 times
multiplier is a rough, back-of-the-envelope hypothetical based on current
knowledge of how the virus is spreading in other places. It assumes that one in
four people who have COVID-19 are symptomatic enough to be tested….Another
researcher, Samuel Scarpino, a Northeastern University professor who
specializes in infectious disease modeling, told the Globe that the U.S. has identified only
between 1 of every 10 cases and 1 in 30 cases.
Similarly, in the Wall Street Journal on March 23,
Stanford researchers Eran Bendavid and Jay Bhattacharya suggested the known
cases were a tiny fraction of the actual number. According to a study by
Bendavid and Bhattacharya,
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….If our surmise of six million
cases is accurate, that’s a mortality rate of 0.01%, assuming a two week lag
between infection and death. This is one-tenth of the flu mortality rate of
0.1%.
On
Friday, the San Francisco Chronicle reported on a new study of
Santa Clara County in California, which suggests that “cases are
being underreported by a factor of 50 to 85”:
If the study’s numbers are
accurate, the true mortality and hospitalization rates of COVID-19 are both
substantially lower than current estimates, and due to lag between infection
and death, researchers project a true mortality rate between .12 and .20.
That
US case fatality rate of 2 to 4 percent commonly reported by politicians
and media outlets is looking less likely every day.
What
Does This Mean for Policy?
If
the Santa Clara study or the estimates of Bendavid and Bhattacharya apply to
the nation overall, then the current count of 710,000 COVID-19 cases in the US
is only a small fraction of the total number of people with the disease. The
true number of cases could number from 35 million to 60 million.
In a
nation with such a large number of infected, efforts to forcibly shutter
businesses and put millions out of work until there are “no new cases, no deaths“—as
suggested by federal health bureaucrat Anthony Fauci—are absurd. This goal is
likely unattainable without completely ending interstate travel and destroying
the US economy over a period of many months, or possibly years.
Moreover,
some epidemiological models models being used by politicians to justify harsh
lockdowns, like the IMHE model,
assume fatality rates based only on “cases reported” to calculate the CFR. This
highlights the highly questionable practice of basing draconian public policy
measures on woefully incomplete government-collected data. From the very
beginning, neither the WHO nor national governments have ever had a handle on
how many cases there are, what the case fatality rate is, or by what means—or
how quickly—the disease spreads.
One
need not know anything about viruses to know from the beginning of the
panic that the process of collecting data for government policymakers tends to
be a biased and makeshift undertaking. This is true of all sorts of data, and
in this case policymakers have never known how many cases there are (or were)
but have nonetheless quoted numbers that suited their political purposes.
Meanwhile, government officials have been encouraging doctors and hospital administrators
to maximize the number of reported deaths due to COVID-19.
Worst
of all, this make-things-up-as-you-go attitude toward COVID-19 numbers is being
draped in the mantle of “science” by bureaucrats and elected officials who
seek to pander to frightened voters. But somewhere
along the line, the United States became a nation where knowing next to nothing
about a disease’s true fatality rate or prevalence is sufficient to justify
abolishing the Bill of Rights and millions of jobs throughout the nation. But
it’s fine, apparently, because this is what the “experts” say we should do.
UPDATE:
April 20
Over
the weekend, several new articles have been published noting increased
prevalence of COVID-19 than previously known (or admitted). The AP reports today :
A flood of new research
suggests that far more people have had the coronavirus without any symptoms,
fueling hope that it will turn out to be much less lethal than originally
feared.
While that’s clearly good
news, it also means it’s impossible to know who around you may be contagious.
That complicates decisions about returning to work, school and normal life.
In the last week, reports of
silent infections have come from a homeless shelter in Boston, a U.S. Navy
aircraft carrier, pregnant women at a New York hospital, several European
countries and California.
In
more than ten states, the proportion of tests that are positive is nearly 20
percent or more.
But
even this may be too low since tests may return false negatives
nearly one-third of the time .
Meanwhile,
one new study in Massachusetts found one-third of people randomly
tested on the street tested positive for COVID-19.
Note: The
views expressed on Mises.org are
not necessarily those of the Mises Institute.
Ryan W. McMaken is the editor of Mises Daily and The Austrian.
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