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A New Understanding of Herd Immunity
The portion of the population that needs to get sick is not fixed. We can change it.https://www.theatlantic.com/health/archive/2020/07/herd-immunity-coronavirus/614035/
Edward Lorenz was just out of college when he was recruited into World War II. He was assigned to be a weather forecaster, despite having no experience in meteorology. What Lorenz knew was math. So he started experimenting with differential equations, trying to make predictions based on patterns in data on past temperatures and pressures. One day, while testing his system, he repeated a simulation with a few decimals rounded off in the data. To his surprise, a radically different future emerged. He called this finding “the butterfly effect.” In a complex model, where each day’s weather influences the next day’s, a tweak in initial conditions can have wild downstream consequences. The butterfly effect became central to the emerging field of chaos theory, which has since been applied to economics, sociology, and many other subjects, in attempts to deconstruct complex phenomena. That field is now helping predict the future of the pandemic—in particular, how it ends.
Chaos theory applies neatly to the spread of the coronavirus, in that seemingly tiny decisions or differences in reaction speed can have inordinate consequences. Effects can seem random when, in fact, they trace to discrete decisions made long prior. For example, the United States has surpassed 125,000 deaths from COVID-19. Having suppressed the virus early, South Korea has had only 289. Vietnam’s toll sits at zero. Even when differences from place to place appear random, or too dramatic to pin entirely on a failed national response, they are not. There is enormous variation even within the U.S., which could also seem chaotic. Some places took limited measures and were barely hit; others locked down but suffered greatly. New York City has been slowly reopening since early June, but despite that—and despite mass outdoor gatherings in the throes of civil unrest over the past six weeks—the city has not seen even a small increase in daily reported cases. By contrast, other cities that have attempted to reopen have seen incapacitating surges.
But just as barely predictable meteorological events arise from totally predictable laws of physics, the complex dynamics of a pandemic center on an extremely limited set of concepts in basic viral biology. Early failures to test and shut down in the U.S. have been amplified through the butterfly effect. Current decisions will be as well. When phenomena appear chaotic, mathematical modelers make it their job to find the underlying order. Once models can accurately describe the real world, as some now do, they gain the predictive power to give clearer glimpses into likely futures. In mid-February, the Harvard epidemiologist Marc Lipsitch told me that this virus could infect most people in the United States if the country’s leaders did not take action. At the time, the U.S. had only a handful of confirmed cases. Few people were imagining the future Lipsitch saw—in which millions, even hundreds of millions, of Americans could fall ill. This was, at least in part, because we weren’t testing for the virus.
Lipsitch even received some criticism from scientists who felt uncomfortable with his estimate, since there were so little data to go on. Indeed, at that point, many futures were still possible. But when a virus spreads as quickly and effectively as this one was spreading in February—killing many while leaving others who had few or no symptoms to spread the disease—that virus can be expected to run its course through a population that does not take dramatic measures. Now, based on the U.S. response since February, Lipsitch believes that we’re still likely to see the virus spread to the point of becoming endemic. That would mean it is with us indefinitely, and the current pandemic would end when we reach levels of “herd immunity,” traditionally defined as the threshold at which enough people in a group have immune protection so the virus can no longer cause huge spikes in disease. The concept of herd immunity comes from vaccination policy, in which it’s used to calculate the number of people who need to be vaccinated in order to ensure the safety of the population. But a coronavirus vaccine is still far off, and last month, Anthony Fauci, the head of the National Institute of Allergy and Infectious Diseases, said that, because of a “general anti-science, anti-authority, anti-vaccine feeling,” the U.S. is “unlikely” to achieve herd immunity even after a vaccine is available.
Back in February, Lipsitch gave a very rough estimate that, absent intervention, herd immunity might happen after 40 to 70 percent of the population had been infected. The idea of hitting this level of infection implied grim forecasts about disease and death. The case-fatality rate for COVID-19 is now very roughly 1 percent overall. In the absolute simplest, linear model, if 70 percent of the world were to get infected, that would mean more than 54 million deaths. But the effects of the coronavirus are not linear. The virus affects individuals and populations in very different ways. The case-fatality rate varies drastically between adults under 40 and the elderly. This same characteristic variability of the virus—what makes it so dangerous in early stages of outbreaks—also gives a clue as to why those outbreaks could burn out earlier than initially expected. In countries with uncontained spread of the virus, such as the U.S., exactly what the herd-immunity threshold turns out to be could make a dramatic difference in how many people fall ill and die. Without a better plan, this threshold—the percentage of people who have been infected that would constitute herd immunity—seems to have become central to our fates. Some mathematicians believe that it’s much lower than initially imagined. At least, it could be, if we choose the right future.
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13 replies
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= new reply since forum marked as read
that I have seen yet. I just hope people take the time to fully read it and do not just jump to all sorts of faulty conclusions.
thank you for your reply btw
