Lately, he’s been replying with a blunt take: “I don’t know.”It’s a short answer that conceals a good deal of nuance. Since the beginning of the pandemic, the job of disease modelers has not been to tell us precisely where we are going, but to prepare us for many possible futures. This is a fraught business. Offering multiple options in a crisis invites people to run away with one conclusion or another as it suits them, leading to too much sacrifice or too much wishful thinking. (Remember when the Trump administration seized upon the most optimistic forecasts to declare that the pandemic would be over by summer—that is, last summer?) Models can help policymakers decide where to put resources, and they can also help people like you and me find some mooring in an uncertain world . Oracles, however, they are not.
The reason is that at any moment in a disease outbreak, a projection may rise or fall exponentially depending on its initial assumptions. Those assumptions are hard to make. In the beginning, epidemiologists were scrambling to understand the very basics of a new pathogen: how the virus spreads between people, how fast it incubates, the role of super-spreaders and asymptomatic infections in seeding a so-called “invisible pandemic.” Over time, they got a better grip, with the help of a full-court scientific press—more virological and immunological data about how the virus infects, and more epidemiological data about what happens next. Once researchers understood how the virus moved, it was easier to determine how to turn it back with things like masks and social distancing .But even with answers, that uncertainty never goes away. Consider the present: Delta itself, of course, has also brought its own set of unknowns related to its faster replication and ability to infect . So has vaccination, including the extent to which vaccinated people spread the virus , and how well immunity holds up over time. These all affect how severe the Delta wave will be at any particular time and place. And, as we settle those questions, there’s always the potential for a new variant to throw any long-term calculations off. “We definitely have more information, but I wouldn’t say the number of unknowns has really decreased,” says Emmanuela Gakidou, a professor of health metrics science at the University of Washington. “I wouldn't say we’ve ever been content that we’ll have a model that will ever be used for more than a week in a row.”
Bergstrom suggests thinking of it this way: In March 2020, how would a disease modeler have predicted the ups and downs that were to come? The pandemic is now said to be in its fourth wave, but the term belies a far more complex topography of stubborn plateaus, gentle bunny hills, and striking peaks. Even in retrospect, the patterns are difficult to explain (and not just because time is now a blur and no longer has meaning). Some changes were due to the virus, and others due to how we responded. During the first wave, public life ground to a halt following national stay-at-home orders. These were replaced by mask mandates and partial, sometimes halting, reopenings.