Coronavirus models aren’t crystal balls. So what are they good for?

“When will this end?” is the question that Michael Z. Levy is most often asked these days.

Levy, an associate professor of epidemiology in the Perelman School of Medicine, is an expert in disease ecology—meaning he studies the ways in which pathogens spread through space and time. He and other researchers around the world have been gathering as much data as they can get their hands on, making calculations, and creating mathematical models—all in an effort to answer critical questions about COVID-19. 

Microscopic coronavirus images superimposed over digital global map

Epidemiologists and data scientists have become unlikely heroes during the coronavirus pandemic. Wave-shaped COVID-19 models that present “best-” and “worst-case” scenarios for the virus trajectory are now commonplace in news stories and on screens behind governors during press conferences. “Flatten the curve” is scribbled on signs that hang in neighbors’ windows. Local governments are using algorithms to make decisions about stay-at-home orders and supplies.   

During a time of unprecedented uncertainty, it makes sense that we would put stock in these models. We are hoping they will be able to forecast the future and offer a glimmer of sunshine. 

But math cannot account for the unpredictability of human behavior, and epidemiologists are not in the business of fortune-telling, Levy cautions.

“A model isn’t a crystal ball to make predictions,” he says. “It’s more like a pensieve—you take what you already have in your brain, you pull it out, and you swirl it around, so that you can better understand the ramifications of your assumptions. That’s all these models are for—to get our head around what’s already going on and what we can do about it.”

This story is by Lauren Ingeno. Read more at Penn Medicine News.