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But it’s also a landscape of shifting frustrations and fatigue, of wild alternations between pessimism and optimism, like last fall, when Americans returned on vacation amid what was then the pandemic’s worst outbreak. And now, despite a summer peak that’s as bad as ever, in many parts of the country, society has largely returned to the status quo. “People are changing their behavior drastically during an ongoing pandemic,” says Bergstrom. “We are constantly updating our beliefs about the seriousness of this situation. “
In some ways, this means that greater experience with the pandemic can create Following uncertainty for modelers, no less. Beliefs and behaviors are now increasingly heterogeneous, varying from state to state and, in some cases, from city to city. Delta has come at a time when people are increasingly polarized as a result of vaccinations and confused as to what that means for how they should behave. “The one-month mask warrants are OK, and the next month is the protests. It’s really hard to predict in advance, ”says Gakidou.
“The dominant theme that continues to complicate matters now is the interplay between disease state, how people react and how people react over time,” says Joshua Weitz, professor who studies biological systems complexes at the Georgia Institute of Technology. It is a perfectly intuitive idea, 18 months after the start of the pandemic, that our individual perception of risk and the behaviors that result from it should have a collective impact on the trajectory of the virus. But that wasn’t universal understanding at first, Weitz notes, when some thought the pandemic would pass quickly. In modeling parlance, the term for this (a relic of 19th century epidemic theory) is Farr’s Law: infections should peak and then decline at relatively even rates, producing a bell curve.
This curve was not going to obey. Last spring, Weitz and others got to see him coming back for the second round. The first wave had not been completely crushed and too many people remained sensitive. Cases peaked, then stuck on the ‘shoulders’ of the curve, declining at a slower rate than many projections suggested, then stabilized at stubbornly high infection rates. The behavior, Weitz hypothesized, was out of step with how models predicted interventions such as stay-at-home orders would work. By studying mobility reports drawn from cell phone data, an indicator of the number of social contacts people experienced, he found that risky behaviors decreased as the number of deaths increased, but then began to rebound earlier. that the turn is turned. “People look around, see the local situation and change their behavior,” Weitz says.
One of the consequences of these reactive behaviors is that it can be difficult to analyze the usefulness of policies such as mask and vaccine warrants. There is a blur between cause and effect – and between government actions and what the public is already doing, as both respond to rising and falling rates of transmission. For example, he says, if you look at the mask warrant calendar instituted last year in Georgia and compare the rates of before and after cases, you might determine that it has had little effect. But what if it was because people realized that the rates of cases were increasing and they had put on their masks preventively earlier? What if they just started staying home anymore? Or if it was the other way around: the requirement went into effect and few people followed the rules, so the masks never got a chance to do their job? “There is clearly a relationship there,” he says. “I can’t claim we got to the bottom of it.”
For modelers, this uncertainty presents a challenge. To assess when the delta’s surge might end, one could look to places where it has already swept through and peaked, such as the United Kingdom. But will it die quickly, or will it take a slower decline, or perhaps stabilize at a constant infection rate? These scenarios, Weitz argues, will depend primarily on how people perceive risk and behave. You would expect the Delta variant to hit and end up backing down differently in high-vaccination Vermont than it did in low-vaccination Alabama. Different policies for schools and businesses will determine how many people from different groups will mix, and be magnified or reduced by how people react independently.
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