Credit: George Mason University
How a computer virus spreads gave George Mason University researcher Cameron Nowzari insight into how human viruses, like the coronavirus, spread.
About six years ago, Nowzari, an assistant professor in the Department of Electrical and Computer Engineering, discovered that his work modeling how a computer virus can take over a computer could be applied to how biological viruses spread among humans. At that time, Ebola was the enemy virus in West Africa. Now, the coronavirus has spread across the world, and Nowzari is using his knowledge to create models that track the virus in a new way.
“My expertise is network systems. I’m taking a closer look at how mobility affects the spread of the virus,” says Nowzari. Nowzari’s research is unique because it takes into account people’s movement between areas. “What’s missing from all of these models are things like airline travel and travel between regions, and I want to bring that perspective in,” he says. He received a National Science Foundation RAPID grant for this work.
Most models that people see cited today in media are lumped population models, meaning experts are looking at predicting the spread of the virus and the infected number of people in one localized region.
Currently, the lumped population models are guiding policy and business decisions across the world. Nowzari hopes that his different models will help guide these decisions as well. “Right now, these policies of reopening are a little too myopic. They aren’t focusing on the networking effects,” he says.
Nowzari also hopes to scale his work so he could create an app for people to use so that they can minimize their own risk of infection. “We want to show people when they are at risk of being infected, when they can go to the grocery store to minimize that risk, and regional data on how their area is being affected.”
“Overall, this could help policy, economic guidelines, and individual people,” says Nowzari.
###
Media Contact
Martha Bushong
[email protected]
Original Source
https:/