How Herd Immunity Happens - The Atlantic - 0 views
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Chaos theory applies neatly to the spread of the coronavirus, in that seemingly tiny decisions or differences in reaction speed can have inordinate consequences.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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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Gomes explains, “There doesn’t need to be a lot of variation in a population for epidemics to slow down quite drastically.”
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in dynamic systems, the outcomes are more like those in chess: The next play is influenced by the previous one. Differences in outcome can grow exponentially, reinforcing one another until the situation becomes, through a series of individually predictable moves, radically different from other possible scenarios. You have some chance of being able to predict the first move in a game of chess, but good luck predicting the last.
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“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.
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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.”
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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
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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.
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Lipsitch also believes that heterogeneity is important to factor into any model. It was one reason he updated his prediction, not long after we spoke in February, of what the herd-immunity threshold would be. Instead of 40 to 70 percent, he lowered it to 20 to 60 percent. When we spoke last week, he said he still stands by that, but he is skeptical that the number lands close to the 20 percent end of the range. “I think it’s unlikely,” he said, but added, “This virus is proving there can be orders-of-magnitude differences in attack rates, depending on political and societal decisions, which I don’t know how to forecast.”
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he believes that the best we can do is continually update models based on what is happening in the real world. She can’t say why the threshold in her models is consistently at or below 20 percent, but it is. “If heterogeneity isn’t the cause,” she said, “then I’d like for someone to explain what is.”
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Biological variations in susceptibility could come down to factors as simple as who has more nose hair, or who talks the loudest and most explosively, and Langwig shares the belief that these factors can create heterogeneity in susceptibility and transmission. Those effects can compound to dramatically change the math behind predictions for the future.
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What’s important to her, rather, is that people are not misled by the idea of herd immunity. In the context of vaccination, herd-immunity thresholds are relatively fixed and predictable. In the context of an ongoing pandemic, thinking of this threshold as some static concept can be dangerously misleading.
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She worries that many people conflate academic projections about reaching herd immunity with a “let it run wild” fatalism. “My view is that trying to take that route would lead to mass death and devastation,” she says.
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Left totally unchecked, Bansal says, the percentage of infected people could go even higher than 70 percent.
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“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,” Bansal says. If a population average led people in those settings to get complacent, there could be needless death.
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Bansal believes that heterogeneity of behavior is the key determinant of our futures. “That magic number that we’re describing as a herd-immunity threshold very much depends on how individuals behave,” Bansal says, since R0 clearly changes with behaviors. On average, the R0 of the coronavirus currently seems to be between 2 and 3, according to Lipsitch.
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Social distancing and other reactive measures changed the R0 value, and they will continue to do so. The virus has certain immutable properties, but there is nothing immutable about how many infections it causes in the real world.
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The threshold can change based on how a virus spreads. The spread keeps on changing based on how we react to it at every stage, and the effects compound. Small preventive measures have big downstream effects
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In other words, the herd in question determines its immunity. There is no mystery in how to drop the R0 to below 1 and reach an effective herd immunity: masks, social distancing, hand-washing, and everything everyone is tired of hearing about. It is already being done.
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“I think it no longer seems impossible that Switzerland or Germany could remain near where they are in terms of cases, meaning not very much larger outbreaks, until there’s a vaccine,” he said. They seem to have the will and systems in place to keep their economies closed enough to maintain their current equilibrium.
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Other wealthy countries could hypothetically create societies that are effectively immune to further surges, where the effective herd-immunity threshold is low.
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We have the wealth in this country to care for people, and to set the herd-immunity threshold where we choose. Parts of the world are illuminating a third way forward, something in between total lockdown and simply resuming the old ways of life. It happens through individual choices and collective actions, reimagining new ways of living, and having the state support and leadership to make those ways possible
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as much attention as we give to the virus, and to drugs and our immune systems, the variable in the system is us. There will only be as much chaos as we allow.