IMPRIMIS: A Sensible and Compassionate Anti-COVID Strategy

The following is adapted from a panel presentation on October 9, 2020, in Omaha, Nebraska, at a Hillsdale College Free Market Forum.

My goal today is, first, to present the facts about how deadly COVID-19 actually is; second, to present the facts about who is at risk from COVID; third, to present some facts about how deadly the widespread lockdowns have been; and fourth, to recommend a shift in public policy.

1. The COVID-19 Fatality Rate

In discussing the deadliness of COVID, we need to distinguish COVID casesfrom COVID infections. A lot of fear and confusion has resulted from failing to understand the difference.

We have heard much this year about the “case fatality rate” of COVID. In early March, the case fatality rate in the U.S. was roughly three percent—nearly three out of every hundred people who were identified as “cases” of COVID in early March died from it. Compare that to today, when the fatality rate of COVID is known to be less than one half of one percent.

In other words, when the World Health Organization said back in early March that three percent of people who get COVID die from it, they were wrong by at least one order of magnitude. The COVID fatality rate is much closer to 0.2 or 0.3 percent. The reason for the highly inaccurate early estimates is simple: in early March, we were not identifying most of the people who had been infected by COVID.

“Case fatality rate” is computed by dividing the number of deaths by the total number of confirmed cases. But to obtain an accurate COVID fatality rate, the number in the denominator should be the number of people who have been infected—the number of people who have actually had the disease—rather than the number of confirmed cases.

In March, only the small fraction of infected people who got sick and went to the hospital were identified as cases. But the majority of people who are infected by COVID have very mild symptoms or no symptoms at all. These people weren’t identified in the early days, which resulted in a highly misleading fatality rate. And that is what drove public policy. Even worse, it continues to sow fear and panic, because the perception of too many people about COVID is frozen in the misleading data from March.

So how do we get an accurate fatality rate? To use a technical term, we test for seroprevalence—in other words, we test to find out how many people have evidence in their bloodstream of having had COVID.

This is easy with some viruses. Anyone who has had chickenpox, for instance, still has that virus living in them—it stays in the body forever. COVID, on the other hand, like other coronaviruses, doesn’t stay in the body. Someone who is infected with COVID and then clears it will be immune from it, but it won’t still be living in them.

What we need to test for, then, are antibodies or other evidence that someone has had COVID. And even antibodies fade over time, so testing for them still results in an underestimate of total infections.

Seroprevalence is what I worked on in the early days of the epidemic. In April, I ran a series of studies, using antibody tests, to see how many people in California’s Santa Clara County, where I live, had been infected. At the time, there were about 1,000 COVID cases that had been identified in the county, but our antibody tests found that 50,000 people had been infected—i.e., there were 50 times more infections than identified cases. This was enormously important, because it meant that the fatality rate was not three percent, but closer to 0.2 percent; not three in 100, but two in 1,000.

When it came out, this Santa Clara study was controversial. But science is like that, and the way science tests controversial studies is to see if they can be replicated. And indeed, there are now 82 similar seroprevalence studies from around the world, and the median result of these 82 studies is a fatality rate of about 0.2 percent—exactly what we found in Santa Clara County.

In some places, of course, the fatality rate was higher: in New York City it was more like 0.5 percent. In other places it was lower: the rate in Idaho was 0.13 percent. What this variation shows is that the fatality rate is not simply a function of how deadly a virus is. It is also a function of who gets infected and of the quality of the health care system. In the early days of the virus, our health care systems managed COVID poorly. Part of this was due to ignorance: we pursued very aggressive treatments, for instance, such as the use of ventilators, that in retrospect might have been counterproductive. And part of it was due to negligence: in some places, we needlessly allowed a lot of people in nursing homes to get infected.

But the bottom line is that the COVID fatality rate is in the neighborhood of 0.2 percent.

Continue reading at:  https://imprimis.hillsdale.edu/sensible-compassionate-anti-covid-strategy

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Jay Bhattacharya is a Professor of Medicine at Stanford University, where he received both an M.D. and a Ph.D. in economics. He is also a research associate at the National Bureau of Economics Research, a senior fellow at the Stanford Institute for Economic Policy Research and at the Freeman Spogli Institute for International Studies, and director of the Stanford Center on the Demography and Economics of Health and Aging. A co-author of the Great Barrington Declaration, his research has been published in economics, statistics, legal, medical, public health, and health policy journals.

 

Reprinted by permission from Imprimis, a publication of Hillsdale College. ©All rights reserved. The opinions expressed may not necessarily reflect the views of The Prickly Pear or of the sponsors.