If we can forecast the path of a hurricane or even the trajectory of a subatomic particle, why shouldn’t we also be able to forecast the spread of an emerging disease? That is the question Alessandro Vespignani, who was installed as Northeastern University’s Sternberg Family Distinguished University Professor of Physics on Tuesday in the Raytheon Amphitheater, began asking 10 years ago.

The answer, he explained, is twofold: our ability to predict disease transmission is limited by both the complexity of human social networks and by the fact that understanding that complexity requires enormous amounts of data and thus extremely sophisticated technology.

Vespignani – who holds joint appointments in the College of Science, the College of Computer and Information Science and the Bouvé College of Health Science and is one of the leading network scientists in Northeastern’s Center for Complex Network Research – said the mobility patterns of individuals around the world determine how quickly and vastly a contagion will spread.

During the Black Plague, for example, which killed half of the European population, people could travel only a few miles a day on average. But nearly 700 years later, with the aid of airplanes, automobiles and an exponentially larger population, people are now traveling many thousands of miles each day.

As a result, today’s social network is much more closely linked, according to Vespignani: A germ picked up in Vietnam could travel to Boston in just a few hours. To understand the macroscopic structure of human systems, Vespignani said, we must look not just at individual behavior, but also at the system as a whole. “With just two molecules of water,” he explained, “we won’t see how water behaves as a liquid or ice. We need millions of molecules to do that.”

In human networks, Vespignani said, the individual is the social atom and groups are considered social molecules. When we examine millions of social molecules, a picture emerges depicting our collective behavior. The image is complex, because human social networks don’t respond as we might expect from external forces such as disease. Computer models, Vespignani noted, can help untangle that complexity.

With information constantly flowing from satellite traceable devices such as mobile phones and flight trackers, Vespignani said, we are in the midst of a “data deluge.” For the first time, our computer infrastructure is powerful enough to analyze that data. Vespignani, for example, can map thousands of individuals’ movements across spaces to generate mobility patterns for the whole system.

Two years ago, his team used their model to project the activity peaks of the H1N1 pandemic for various regions in the Northern Hemisphere. Eventually the researchers were able to validate those projections with actual data and found that their “simulations were spot on.”

Vespignani hopes that with other forms of data, like those generated by Internet and social network use, it will be possible to project “not just the spread of disease, but also ideas, knowledge, or the evolution of languages,” he explained.

Northeastern researchers, Vespignani noted, “are keen to create new results now…to see those systems in completely different ways.” His disease model, he said, is a prototype for potentially limitless applications.

Vespignani was elected to the physics and engineering sciences branch of the Academy of Europe last year for his research on the spread of epidemics.

President Joseph E. Aoun said the world-renowned statistical physicist “epitomizes what we’ve been doing” in terms of interdisciplinary collaborations.

Murray Gibson, dean of the College of Science, introduced Vespignani, saying he caps off Northeastern’s team of network scientists, which is made up of world leaders in the field.

Provost Stephen W. Director, who oversaw the formal installation of the new Sternberg Chair, made one last prediction toward the close of the event, noting that we “will be seeing many more installations of Northeastern faculty chairs, creating their own interconnected network of interdisciplinary work.”