Editor's note: This story was updated at 12:00 a.m. E.D.T. on Sunday, April 19
Way more people may have gotten coronavirus than we are detecting.
That's the takeaway from a small study of coronavirus antibodies in more than 3,000 people in Santa Clara County, California. The results suggested that between 2.5% and 4.2% of people in the county have contracted COVID-19, which is 50 to 85 times greater than the number of cases being reported at the time. Not everyone is convinced the true prevalence is that high, however, with some saying the antibody test the researchers used was not reliable.
However, this type of antibody testing, or serologic study, should be rolled out more broadly, epidemiologists told Live Science.
"I think this is a great start to beginning a serologic survey in the U.S., and I agree that we should expand this testing as much as possible so that hopefully we can figure out what level of antibodies, if any, is necessary to maintain immunity," said Krys Johnson, an epidemiologist at Temple University in Philadelphia.
So what does this mean for how deadly the virus is, how widely it has spread, and when we can ease social distancing? The answers aren't straightforward, epidemiologists told Live Science.
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The results
First, the study: Stanford University researchers used Facebook ads to find volunteers to be tested for antibodies to the novel coronavirus, or proteins produced by a person’s immune system to fight off a specific virus that has invaded the body. Roughly 3,300 of those volunteers came to a drive-through testing site on April 3 and April 4. One in every 66 tested positive for antibodies to the novel coronavirus. White women and affluent people were overrepresented in the population, while Latinos and Asians were underrepresented compared with Santa Clara's overall population.
A total of 50 tests came back positive. After adjusting for differences in zip code, race and sex between the sample population and Santa Clara as a whole, the researchers estimated that between 48,000 and 81,000 people in the 2-million-strong county had contracted coronavirus at some point. At the time, the health department was reporting about 1,000 positive cases.
The findings were posted Friday (April 17) to the preprint database medrXiv; they have not gone through peer review.
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Less deadly than thought?
Using their data, the team estimated that the true "infection fatality rate" of coronavirus — or the number of infected people who die from the disease — is between 0.12% and 0.2%, or between 20% and two times more deadly than seasonal influenza (which kills about 0.1% of people it infects, on average). Other studies have estimated infection fatality rates between 0.5% and 0.9%, Nature news reported.
Some experts have questioned the results, saying that when few people in a population have the virus, even a few false positives on the test could create the impression that there are many more coronavirus cases than actually exist, according to Nature.
The test used in this study has not been approved by the Food and Drug Administration (FDA) yet.
"They are constrained by the fact that the antibody tests they used were not very good, which they had to try and adjust for" who were infected, said George Rutherford, professor of epidemiology and biostatistics at the University of California, San Francisco (UCSF).
"The market's been flooded with these tests." Rutherford told Live Science. "But the FDA has relaxed its rules so there's not the same degree of quality control."
The crude rate of positives they found before making adjustments — about 1.5% — is probably about right, Rutherford said. However, using statistical adjustments to arrive at the range of 2.5% to 4.2%, and then to infer fatality rates, was likely a stretch, he added.
"The interpretation of the ratio of cases to death is an over-interpretation," Rutherford told Live Science. "
What's more, because they didn't take a random sample, the study is subject to what's called selection bias, Rutherford said.
"They may have picked off a piece of the population that was more likely to be infected or less likely to be infected, we just don't know," Rutherford said. (An example of potential selection bias: if someone suspected they had been infected earlier, but couldn't be tested when symptomatic, they might be more motivated to pursue antibody testing.)
Johnson, meanwhile, thinks the true prevalence in Santa Clara could be even higher.
"I think if they'd had an ethnically representative sample in this study as they'd hoped, they may have found an even higher proportion of people with antibodies, based on current reports that minorities are disproportionately affected by COVID-19," Johnson told Live Science in an email. "This would mean that even the informative conclusions here are still a conservative estimate of the likely number of infected people in Santa Clara County and throughout the U.S."
But on the other hand, the infection fatality rate in Santa Clara can't be directly translated to other spots in the U.S., which face higher rates of obesity and other chronic conditions known to worsen the outcomes of COVID-19. So infection fatality rates in other American cities may be higher than the Santa Clara County estimate, Johnson said.
Ultimately, it's just one sample in a single locale, said Dr. William Schaffner, an infectious diseases specialist at Vanderbilt University in Tennessee.
Schaffner suspects the 50 to 85 times higher prevalence "is on the high side" — meaning the true infection fatality rate could potentially be higher. But without doing antibody testing in several other places and populations, there is no way to know for sure, Schaffner told Live Science.
Mild disease and catastrophic impacts
If the numbers are in fact representative, though, how can this relatively low fatality rate be reconciled with the catastrophes that have unfolded around the world? How can a disease that's only slightly more deadly than the flu have caused China to shut down its economy for two months, brought the country's largest city to the brink of collapse, and kept 1.5 billion children out of school?
It turns out, that's definitely possible, because before late last year, no one on Earth had been exposed to this virus, so everyone could catch it. By contrast, many people will be immune to viruses that have circulated before, and only a fraction of the population is susceptible to catching those. Even if the novel coronavirus virus is not that deadly, it could kill many more people than a known, but similarly deadly bug simply because it has the potential to infect a greater proportion of the population. That can easily overwhelm the health care system, Schaffner said.
The flip side of this data is that nowhere in the U.S. is likely to have most of its population exposed to SARS-CoV-2 at this point, Schaffner told Live Science. So the idea of us being close to "herd immunity" — when enough people have gotten the virus and are immune that the disease can no longer spread — is wishful thinking.
In Santa Clara, at least 95% of the population is still susceptible to the virus, Schaffner said. "So we can't depend on any kind of herd immunity to slow down this virus yet."
Extrapolating data from one locale to another is always statistically dicey, but even in New York City — where reported deaths from COVID-19 already exceed 0.1% of the city's population — some other numbers suggest that about 15% of the population has been infected. That's well below what's needed to naturally slow the spread of coronavirus, Johnson said.
That said, the numbers do suggest caution before mandating social distancing too far out into the future based on epidemiological models, especially without taking into account practical factors, such as the societal costs of social distancing, Schaffner said. (Some health experts have suggested some form of social distancing may linger into 2022, unless a vaccine becomes available sooner.)
"Social distancing, into the fall and winter, I think is reasonable, and then let's see," Schaffner said.
Editor's Note: This story was updated to include comment from George Rutherford.
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Originally published on Live Science.
