Virtual Baltimore Lab
Like so many cities across the United States—and the world—Baltimore has seen a steep increase in obesity, particularly in low income neighborhoods. Increasingly, scientists understand that the problem stems from a complicated system of factors. The challenge is to understand the system itself. That, however, is no easy feat.
In the Global Obesity Prevention Center (GOPC), scientists use computational simulation models to understand complex systems and processes by evaluating the multiple factors at play within them. By combining information from different sources and disciplines—such as statistics, epidemiology, nutrition, sociology and economics—simulation models examine complex problems like obesity by considering multiple factors and influences at once. Agent-Based Models (ABMs), which treat each person potentially impacted by obesity as an individual simulated “agent” appear particularly promising when it comes to developing systems-focused solutions to the obesity epidemic.
“The biggest job in building this kind of model is making it represent reality as much as possible,” says Yeeli Mui, PhD student and systems science trainee. “The main plus of this kind of simulation, though, is the time and resource savings. If you actually go out into the field, it takes a lot of resources and time. It’s a huge investment. Models like this allow us to test in a simulated environment.”
“Historically this type of modeling has been used in economics and business models. We are adapting it into the public health arena,” Mui says. “We’ve introduced models to various stakeholders and received positive feedback and interest. For instance, when policymakers are trying to put forth bills, there’s often a lack of evidence to support their case. This would be a useful tool in providing evidence of what kind of outcomes we might expect if a policy were to be implemented.”