When first signs of a disease outbreak occur, epidemiologists, health workers, politicians and scientists rely on sophisticated forecasting models to determine how the disease is spreading and what should be done to minimize the risk of infection. A collaborative research between Politecnico di Torino and the New York University School of Engineering Tandon is revolutionizing the traditional modeling process, getting forecasts, that are easier to calculate and more effective in a hyper-connected world.

All forecasting models correlate the movement of a disease with the population over time, but current simulations do not always consider effectively a matter of course: the mobility and the activity vary among people and these variations have an impact on chances of contracting or spreading the disease.

A new model has been presented in a paper published in the prestigious journal Physical Review Letters by Alessandro Rizzo, associate professor of the Department of Control and Computer Engineering of the Politecnico and visiting professor at New York University School of Engineering Tandon, Lorenzo Zino, PhD student of the Politecnico in pure and applied mathematics, and Maurizio Porfiri, professor of mechanical and aerospace engineering at New York University School of Engineering Tandon.

In the future, researchers expect this model to help professionals in this sector face an outbreak, also by implementing vaccination strategies, considering risks and benefits arising from travel bans and measuring the effectiveness of disease prevention campaigns.