New models suggest COVID-19 herd immunity might be achieved with far fewer people infected

People line up for COVID-19 tests in New York
(Image credit: Spencer Platt/Getty Images)

COVID-19 researchers and modelers have assumed that at least 60 percent of a population, perhaps 70 percent, would need to be infected with the new coronavirus or vaccinated against it before reaching herd immunity, the point at which the virus can no longer spread widely among a community. Some infectious disease experts are now examining the "hopeful possibility" that far fewer people have to get infected or immunized to achieve herd immunity, The New York Times reports, citing interviews with more than a dozen scientists.

If their new, complicated statistical models are correct, and communities can reach herd immunity with 50 percent or less of people gaining immunity to COVID-19, "it may be possible to turn back the coronavirus more quickly than once thought," the Times reports. A clear minority of researchers predict as few as 10 or 20 percent of a population developing antibodies to the disease would be sufficient for herd immunity; Stockholm University mathematician Tom Britton calculated the threshold at 43 percent.

It's not clear any city or pocket of a city has sufficient immune people to thwart a second wave of COVID-19, but there may be parts of Mumbai, London, and New York that are close or have developed at least significant collective resistance. In hard-hit New York City, for example, fewer than 1 percent of people being tested in some neighborhood clinics over the past eight weeks have tested positive, the Times notes. Even a lower threshold "means many residents of the community will have been sickened or have died, a high price to pay for herd immunity."

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And right now, the models are untested. "Mathematically, it's certainly possible to have herd immunity at these very, very low levels," Carl Bergstrom, an infectious disease expert at the University of Washington, tells the Times. "Those are just our best guesses for what the numbers should look like," but "they're just exactly that, guesses."

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