Viruses, such as the one that causes COVID-19, spread quickly through large cities because of a complex web of interactions between people taking place in a densely populated area. But how viruses move from person to person in smaller, rural communities is less well understood, resulting in public health and economic decisions that are made on the basis of scant information and overgeneralized modeling.

A team led by Whiting School of Engineering computer scientist and cybersecurity expert Anton Dahbura is developing a new model that more accurately understands and predicts the spread of diseases such as COVID-19 in both large and small communities.

“The bottom line is, you cannot treat a population as a big barrel of marbles; infection is a very localized and personalized process,” says Dahbura, who is a member of the Malone Center for Engineering in Healthcare and the Johns Hopkins Institute of Assured Autonomy, as well as the executive director of the Johns Hopkins University Information Security Institute. “Yet current models are very generalized and don’t consider different types and sizes of communities or the lag time of spread across vast geographical areas. Our model considers both.”

Developed with the help of Mathias Unberath, an assistant research professor in Computer Science and a member of the Malone Center, along with Lingxin Hao and Emily Agree of the Johns Hopkins Population Center, the new model envisions populations in terms of extremely localized and specific “modules” of people, their dwellings, and shared community spaces such as offices, schools, stores, and restaurants. It takes into account that people interacting with one set of modules may also interact with another, acquiring and spreading infection.

“The idea is to connect one module to another, in a hierarchical fashion, so they fit together like LEGO bricks, creating an entire system that goes from individual dwellings and communities to cities, states, and beyond,” Dahbura explains.  Read more at The Hub.