Wells: What does that mean?
Hamblin: She thinks that this is not a model where we really see predictable outcomes. You might have a sick person go onto a plane and infect dozens of others, and you might have a sick person and fly and create no more cases. Those events start to seem sort of random.
Wells: And why would that be? Do we have theories?
Hamblin: We don’t know. These are things we’ll find out later. But one of the researchers mentioned that it could be something like the density of people’s nose hairs.
Wells: The case for nose hairs. [Laughs]
Hamblin: That’s just one of many variables. There are at least some physiological differences that will make one person more likely to become infected and have the virus thrive and spread within them.
Wells: Right. Does that mean there’s some percentage of people that just aren’t susceptible for some reason?
Hamblin: No, everyone is susceptible. But even a small variation in how susceptible you are to a given exposure makes broader prediction difficult. And that’s just one thing that makes prediction difficult. You also have these super-spreaders, who are shedding a ton, while some people might be shedding very little. Some people come in contact with tons of other people, and some people totally isolate. Once you start trying to make models about what the level of herd immunity would be, you have to factor in all these variables that are different than if you were just vaccinating everyone.
Wells: Is this what chaos theory helps explain?
Hamblin: Well, chaos theory comes into play when you’re looking at these outcomes that don’t seem possible. Mathematicians like Gomez try to find order and make predictions within that system even when things seem random. The field of chaos theory grew out of findings from applying mathematics to try to predict weather, which is very complicated. It should be really predictable, but it’s extremely difficult to predict because a slight change in one circumstance has huge downstream effects.
Wells: Is that like “A butterfly flaps its wings and a year later there’s a tsunami”?
Hamblin: Exactly. It is called the butterfly effect. Whenever someone made a decision to get on the first international flight, that wasn’t just one act. It had massive consequences. And when effects of single actions compound like that, and also when you have these many variables for potential outcomes, models vary really dramatically. That’s not to explain exactly why 20 percent is right, but it explains why it’s possible.
Wells: This is so frustrating because it’s like there could be an extremely hopeful thing but no guarantees so we can’t really do anything about it.
Hamblin: I think this is helpful—it’s information about how this is working. And I actually think it is actionable, because it tells us that we have the capacity to change this threshold. It depends, in large part, on us. There is a sort of fatalism in advocating, Oh, just let it run wild because we’re going to hit the same number of deaths no matter what.