BROOKLYN, N.Y., Nov. 28, 2016 /PRNewswire-USNewswire/ -- 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.