Here’s today’s much-awaited PDF preprint (not peer reviewed) by a Stanford team that performed blood antibody tests on a fairly representative sample of 3,330 Santa Clara County residents on April 3-4. Stanford professor authors Bendavid, Bhattacharya, and Ioannidis have been prominent skeptics of the recent doom-oriented conventional wisdom.
Eran Bendavid, Bianca Mulaney, Neeraj Sood, Soleil Shah, Emilia Ling, Rebecca Bromley-Dulfano, Cara Lai, Zoe Weissberg, Rodrigo Saavedra, James Tedrow, Dona Tversky, Andrew Bogan, Thomas Kupiec, Daniel Eichner, Ribhav Gupta, John Ioannidis, Jay Bhattacharya
Background: Addressing COVID-19 is a pressing health and social concern. To date, many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters. We measured the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County.
As you’ll recall, a PCR nasal swab test is intended to measure whether you are infected right now, while an antibody blood test is intended to measure whether you have ever been infected (or perhaps not counting the most recent days).
Methods: On 4/3-4/4, 2020, we tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a representative sample of the county by demographic and geographic characteristics. We report the prevalence of antibodies to SARS-CoV-2 in a sample of 3,330 people, adjusting for zip code, sex, and race/ethnicity. We also adjust for test performance characteristics using 3 different estimates: (i) the test manufacturer’s data, (ii) a sample of 37 positive and 30 negative controls tested at Stanford, and (iii) a combination of both.
Results: The unadjusted prevalence of antibodies to SARS-CoV-2 in Santa Clara County was 1.5% (exact binomial 95CI 1.11-1.97%), and the population-weighted prevalence was 2.81% (95CI 2.24-3.37%). Under the three scenarios for test performance characteristics, the population prevalence of COVID-19 in Santa Clara ranged from 2.49% (95CI 1.80-3.17%) to 4.16% (2.58-5.70%).
For all its biomedical technology, California had shamefully low testing percentages, among the worst in the country until late March. So its Case Fatality Rate (deaths/confirmed cases) was much exaggerated relative to its Infection Fertility Rate (deaths/actual infections).
But everybody already knew that Confirmed Cases was much lower due to lack of testing than any plausible estimate of actual cases.
These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases.
Today, Santa Clara County has 1,870 official cumulative cases, with a current doubling rate of every three weeks, one of the lower in the country. Today, it has 73 official deaths. So that’s a Case Fatality Rate of 3.9% (which I don’t think anybody believes anymore).
Conclusions: The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases. Population prevalence estimates can now be used to calibrate epidemic and mortality projections.
On the other hand, that looks like a long way from herd immunity, which is usually assumed to be over 50%.
There have been two popular arguments for optimism:
- Infection Fatality Rate has been much lower than the Case Fatality Rate
- We are practically to Herd Immunity already
This new paper seems to support the first proposition, but not the second.
On the other other hand, Silicon Valley has been surprisingly lightly hit for a place with a lot of contact with China and some of the earliest known infections in the U.S.
Presumably, places like NYC have higher infection rates. One study from a New York City hospital of women coming in for childbirth found a 15% infection rate, although one commenter alleged that hospital had a lot of Ultraorthodox patients, who have seemingly been hard hit.
By the way, even a study like this one that made an effort to reduce self-selection will still have problems with self-selection, although it’s hard to guess how they will bias results. They probably got a lot of people who signed up for the drive-thru because they are worried they might have the virus, perhaps because they are out and about. They perhaps got fewer hard-core isolaters and fewer What-Me-Worry? Alfred E. Neumans.
Mandatory maternity ward testing is probably a decent way to get around self-selection, at least for a sample of youngish women, which can then be adjusted for demographics (since we know a lot about the demographics of new mothers).
Here is the March 24 Wall Street Journal op-ed by Professors Bendavid and Bhattacharya
Current estimates about the Covid-19 fatality rate may be too high by orders of magnitude.
By Eran Bendavid and Jay Bhattacharya
March 24, 2020 6:21 pm ET
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.
,,, An epidemic seed on Jan. 1 implies that by March 9 about six million people in the U.S. would have been infected. As of March 23, according to the Centers for Disease Control and Prevention, there were 499 Covid-19 deaths in the U.S. If our surmise of six million cases is accurate
Ten days later, Santa Clara County had 1% to 6% infected, which would be 3 million to 20 million nationwide, although it’s hard to guess how Silicon Valley compares to the rest of the country: it has a very high percentage of world travelers, but it also went heavily for work-from-home fairly early in March and has a population that is way above the American average in the cognitive power to grasp and follow new rules. I’m guessing Santa Clara County (Palo Alto and San Jose) is the highest average IQ large county in the country, or at least up there with some DC suburban counties.
, 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%. Such a low death rate would be cause for optimism.
But now we are up from 499 deaths to 31,647 deaths.
Anyway, this valuable new data suggest that Case Fatality Rates aren’t all that high (absent hospitals being overwhelmed), but Herd Immunity is still a long way off.
My opinion is the big, unexpected development is that hospital capacity has proven more elastic than was assumed.
Strikingly, this didn’t happen so much by expanding medical care supply as by depressing medical care demand. Non-COVID trips to the emergency room went way down, and NYC doctors innovated a new protocol for treating COVID patients with breathing problems (put them on their stomachs and treat with oxygen masks) that cut ventilator demand substantially. Because caring for patients on ventilators is hugely labor-intensive, that in turn cut demand for doctors and nurses to far below earlier estimates.
So, we appear to have Flattened the Curve.