When COVID-19 became a global pandemic, LSA Assistant Professor Luis Zaman felt the need to provide data to help people navigate it. “I felt like my skill set as a researcher was close but not close enough to actually contribute to the COVID-19 conversation,” says Zaman, “and it was wearing on me. My expertise is not in public health or epidemiology, so I tried to figure out if there was something else I could offer.”

That’s when Zaman, who teaches in the Department of Ecology and Evolutionary Biology and in the Center for the Study of Complex Systems, thought about the computational simulation he’d developed for the students in the "Ecology and Evolution of Infectious Diseases" course that he would teach for the first time in the fall. In preparing the course, he’d wondered if a computational simulation could help his students develop an intuitive ability to understand biological patterns. Such a simulation would need to be simple enough to run within a web browser and flexible enough to introduce variables that could influence the ways a virus might spread. He wasn’t able to find the kind of simulation he’d envisioned, so he decided to build one himself. “I wanted a way for students to experiment and create their own intuition and understanding of the complex problems and processes we see with diseases.” Now he realized the simulator he had created for his students could be adapted for use by the general public, who were suddenly faced with making countless, high-stakes decisions about a situation with which they had no experience. “I don’t think you can really understand the world until you poke it,” says Zaman. “And you can’t really poke a pandemic.

“But if you have a simulation that captures a lot of its dynamics,” he continues, “you can poke it and build a set of expectations about how a disease moves and then start to build an internalized intuition for how to behave.”

Understanding the Impact

Zaman’s simulator allows users to change variables that reflect specific situations—like hanging out with friends, living in an apartment complex, going to the grocery store—and watch the way their decisions influence how the infection spreads over time. It opens with a square, which represents a location, that is filled with purple dots that stand in for people. As different scenarios play out, the dots move around randomly with variable speed and frequency based on the details of the scenario. As the dots bump into each other, the border around some of them begins to change color from black to orange to red to green.

These different border colors represent statuses, from susceptible to exposed to infected to recovered. The dots move and collide very quickly, so a separate chart shows a timescale of the simulation and the status of each dot over a period of time. And because people spread throughout multiple locations, the simulator also shows how dots move and collide with other dots over the span of several boxes.

An animated image that illustrates infection risk

The simulator does not predict how COVID-19 specifically might spread, but it is designed to provide a visual understanding of basic disease dynamics. “We can model these little balls bouncing around the world and think about how a pathogen might spread through a population,” says Zaman. “This helps people intuit how their actions can influence the spread of a disease.”

In his research, Zaman investigates the basic dynamics and outcomes of co-evolution by examining the way viruses and organisms interact. “These relationships can lead to really fascinating co-evolutionary dynamics,” Zaman explains. “When two different organisms are trying to survive and one’s survival happens to be at the expense of the other, how do they co-evolve?”

There’s another benefit to this kind of work, too. Because computational modeling generates large datasets, it allows researchers like Zaman to investigate evolutionary dynamics at a much faster pace than is possible when solely relying on live cell experiments in a lab.

“The principles of evolution and biological processes have been applied to solve computational and technological problems for a long time,” says Zaman. “But computational modeling can also help further investigate and answer questions about evolution, which is still so mysterious. It’s a two-way street.”

Zaman hopes that by providing scenarios that demonstrate how the virus spreads through the public-facing simulator that more people will understand the impact of their choices. “I've been trying to make it really easy to create all these different scenarios, especially as lockdown rules start to change,” he says. “Let's say there's a new policy and we want to see how we’re going to trace contacts. You could put the specifics of that situation into the simulator and show the public what introducing that policy change would look like, so they can make better informed decisions in their daily lives.”

Whether it’s used in the classroom or adopted by the public, Zaman’s intention with the simulator is to offer help based on what he knows best: his research. “Providing an educational tool has always been the goal,” he says.



Images by Julia Lubas