In normal times, herd immunity is calculated based on a standardized intervention with predictable results: vaccination. Everyone is exposed to the same (or very similar) immune-generating viral components. We are able to calculate what percentage of people need that exposure in order to develop meaningful immunity across the population.
This is not the case when a virus is spreading in the real world. Instead, the complexities of real life create what modelers refer to as
heterogeneity. People are exposed to different amounts of the virus, in different contexts, via different routes. A virus that is new to the species creates more variety in immune responses. Some of us are more susceptible to being infected, and some are more likely to
transmit the virus once infected. Even small differences in individual susceptibility and transmission can, as with any chaos phenomenon, lead to very different outcomes as the effects compound over time, on the scale of a pandemic....
[Gabriela Gomes] describes a model in which everyone is equally susceptible to coronavirus infection (a homogeneous model), and a model in which some people are more susceptible than others (a heterogeneous model). Even if the two populations start out with the same average susceptibility to infection, you don’t get the same epidemics. “The outbreaks look similar at the beginning. But in the heterogeneous population, individuals are not infected at random,” she told me. “The highly susceptible people are more likely to get infected first. As a result, the average susceptibility gets lower and lower over time.”
Effects like this—“selective depletion” of people who are more susceptible—can quickly decelerate a virus’s spread. When Gomes uses this sort of pattern to model the coronavirus’s spread, the compounding effects of heterogeneity seem to show that the onslaught of cases and deaths seen in initial spikes around the world are unlikely to happen a second time. Based on data from several countries in Europe, she said, her results show a herd-immunity threshold much lower than that of other models.
“We just keep running the models, and it keeps coming back at less than 20 percent,” Gomes said. “It’s very striking.”
If that proves correct, it would be life-altering news. It wouldn’t mean that the virus is gone. But by Gomes’s estimates, if roughly one out of every five people in a given population is immune to the virus, that seems to be enough to slow its spread to a level where each infectious person is infecting an average of less than one other person. The number of infections would steadily decline. That’s the classic definition of herd immunity. It would mean, for instance, that at
25 percent antibody prevalence, New York City could continue its careful reopening without fear of another major surge in cases.
“If there is a large variability of susceptibility among humans, then herd immunity could be as low as 20 percent,” [Tom] Britton told me. But there’s reason to suspect that people do not have such dramatically disparate susceptibility to the coronavirus. High degrees of variability are more common in things such as sexually transmitted infections, where a person with 100 partners a year is far more susceptible than someone celibate. Respiratory viruses tend to be more equal-opportunity invaders. “I don’t think it will happen at 20 percent,” Britton said. “Between 35 and 45 percent—I think that would be a level where spreading drops drastically.”
...Any such herd-immunity threshold is context-dependent and constantly shifting. It will change over time and space. It varies depending on
the basic reproduction number—the average number of new infections caused by an infected individual. During the early stage of an outbreak of a new virus (to which no one has immunity), that number will be higher. The number is skewed by super-spreading events, such as when one person in a choir infects 50 others. And the number in a dense city such as New York should be expected to be higher than that in rural Alaska. “Within certain populations that lack heterogeneity, like within a nursing home or school, you may even see the herd-immunity threshold be above 70 percent,” [Shweta] Bansal says. If a population average led people in those settings to get complacent, there could be needless death...
