When scientists try to predict the spread of something across populations—anything from a coronavirus to misinformation—they use complex mathematical models to do so.
When scientists try to predict the spread of something across populations—anything from a coronavirus to misinformation—they use complex mathematical models to do so. Typically, they’ll study the first few steps in which the subject spreads, and use that rate to project how far and wide the spread will go.
But what happens if a pathogen mutates, or information becomes modified, changing the speed at which it spreads? In a new study appearing in this week’s issue of Proceedings of the National Academy of Sciences (PNAS), a team of Carnegie Mellon University researchers show for the first time how important these considerations are.
“These evolutionary changes have a huge impact,” says CyLab faculty member Osman Yagan, an associate research professor in Electrical and Computer Engineering (ECE) and corresponding author of the study. “If you don’t consider the potential changes over time, you will be wrong in predicting the number of people that will get sick or the number of people who are exposed to a piece of information.”
Read more at College of Engineering - Carnegie Mellon University
