Hacking Epidemics in a Hyper-Connected World

By NYU Tandon School of Engineering, Special for  USDR

At the first hints of a disease outbreak, epidemiologists, health care providers, policy makers, and scientists turn to sophisticated predictive models to determine how an illness is spreading and what should be done to minimize contagion. A research collaboration between the New York University Tandon School of Engineering and Politecnico di Torino in Italy is upending the traditional modeling process, yielding predictions that are both simpler to calculate and more attuned to a hyper-connected  world.

All predictive models correlate the movement of an illness through a population over time, but current simulations fail to account for a seemingly obvious idea: that mobility and activity varies among people, and that these variations impact the likelihood of contracting or spreading an  illness.

A new paradigm was explained in a paper published in Physical Review Letters by Maurizio Porfiri, a professor of mechanical and aerospace engineering at NYU Tandon, Alessandro Rizzo, a visiting professor at NYU Tandon and an associate professor of control engineering at Politecnico, and Lorenzo Zino, a Politecnico doctoral student in pure and applied  mathematics.

The researchers assume that some people are more active, some less so, and their model accounts for how these differences may impact disease spread. Their approach permits nuanced modeling of different illnesses — from a highly contagious airborne virus such as influenza, which moves quickly among people with high mobility but is limited by those who seclude themselves, to a virus like HIV, which has a long latency period and slower transmission  rate.

“The way I move is the way I catch a disease,” said Porfiri. “We’re changing the point of view from which we start outbreak simulations because we can’t understand how a small outbreak evolves into an epidemic without understanding how different people’s activity levels help spread  it.”

Several traditional models assume homogeneity within the community. “It’s like sick people are all in a specific place, connecting with a set number of people, and that’s not realistic,” said Rizzo. “Some people make more connections than others, and the scale of those connections may be comparable to the scale of the  disease.”

Porfiri and Rizzo explained that traditional simulations use a “discrete time/continuous activity” approach, which typically requires extensive and lengthy simulations. The researchers employ simpler systems of coupled differential equations that allow for the manipulation of factors that can influence disease  spread.

This is the first piece of research to emerge from a three-year, $375,000 National Science Foundation grant awarded to the team to study the concurrent evolution of the dynamics of infectious diseases and the networks through which they spread. The research was also funded in part by grants from the U.S. Army Research Office (ARO) and Compagnia di San  Paolo.

The team has developed one of the few disease modeling approaches that uses heterogeneities in activity levels as a factor in spreading disease. In experiments to test their model, the team successfully predicted the movement of influenza on a university campus and the spread of a trending topic on  Twitter.

“We have infinite possibilities to see the impact of interventions,” said Porfiri. “We can understand how vaccines, quarantine, or other parameters influence contagion. Some illnesses catch fire, while others are quashed immediately. This framework allows for analysis of why and how that  happens.”

In the future, the researchers expect that this model will aid management efforts during an outbreak, including implementing vaccination strategies, evaluating the risks and benefits of travel bans, and gauging the effectiveness of disease prevention  campaigns.

Continuous-Time Discrete-Distribution Theory for Activity-Driven Networks, is  at http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.117.228302.

About the NYU Tandon School of  Engineering
The NYU Tandon School of Engineering dates to 1854, when the New York University School of Civil Engineering and Architecture as well as the Brooklyn Collegiate and Polytechnic Institute (widely known as Brooklyn Poly) were founded. Their successor institutions merged in January 2014 to create a comprehensive school of education and research in engineering and applied sciences, rooted in a tradition of invention, and entrepreneurship and dedicated to furthering technology in service to society. In addition to its main location in Brooklyn, NYU Tandon collaborates with other schools within the country’s largest private research university and is closely connected to engineering programs in NYU Abu Dhabi and NYU Shanghai. It operates business incubators in downtown Manhattanand Brooklyn and an award-winning online graduate program. For more information, visit  http://engineering.nyu.edu.

SOURCE NYU Tandon School of  Engineering

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