Coronavirus Infections May Not Be Uncommon, Tests Suggest

Two new studies using antibody tests to assess how many people have been infected with the coronavirus have turned up numbers higher than some experts had expected.

Both studies were performed in California: one among residents of Santa Clara County, south of San Francisco, and the other among residents of Los Angeles County. In both cases, the estimates of the number of people infected in those counties were far higher than the number of confirmed cases.

The studies, conducted by public health officials and scientists at Stanford University and the University of Southern California, have earned the ire of critics who questioned both the recruitment methods and the analyses.

In the Santa Clara County study, researchers tested 3,330 volunteers for antibodies indicating exposure to the virus. Roughly 1.5 percent were positive.

To find participants in the Los Angeles County study, researchers used a random sample of email addresses and telephone numbers of residents to invite people to participate in what they said would be a Covid-19 survey.

The scientists set limits on participation, based on factors like ethnicity and age, in an effort to ensure the sample represented the county’s population. About 1,100 people signed up to be tested.

The study in Santa Clara County found participants by advertising on Facebook. Most of the first volunteers were in wealthy ZIP codes, so the investigators also recruited from poorer areas. As in the Los Angeles County study, the goal was to get a representative sample of the population.

“It’s not perfect, but it’s the best science can do,” said Dr. John Ioannidis, a professor of medicine at Stanford University and an author of the Santa Clara County report.

Antibody studies in other countries have produced similar figures, he noted.

If the numbers prove accurate, the coronavirus may be much less deadly than originally expected, with a fatality rate more closely resembling that of a seasonal flu strain than a pandemic of profound lethality.

But there are questions about who was tested. Among them: Were people who had reason to think they had been infected overrepresented among the volunteers?

“We tried to look into the possibility of bias influencing the results,” Dr. Ioannidis said. “We asked for symptoms recently and in the last few months, and were very careful with our adjustments. We did a very lengthy set of analyses.”

“We can model the scenarios,” he said. “We should not make decisions just based on I.C.U. mortality.”

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